Analyzing Marketing Campaigns with pandas - solutions.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Analyzing Marketing Campaigns with pandas - solutions.ipynb","provenance":[],"collapsed_sections":["d5AME3E2oefR","Kwnpj8tso8d8","pIBWkDI7pcfw","S5Fqj64gp8zB","pHHQ07CUrQOu","J1YwbaRAtd5G","PwCBev2OuBTT","9dSAT0gzuQ4l","AIznYIITueO2","xzlwPmeZusko","ACIZhRz4vAfD","JP5lBZO-vCXD","Xth6ckQGwr5A","8B757grxxsD7","xKNzpDhYyCYp","i3qf_2XLyVS8","8SdFvY7gyoc3","4dgga4g6yxs4","ZA2-vC_ez5A4","j57f9Y2f0-mo","4lE5ZYMn1SNb","5O9PdTc61sKr","wPdMDpKN12lU","PZnNpBR614k3"],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"SFzOappBfKPA","colab_type":"code","colab":{}},"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9mgJ8yIpfMsh","colab_type":"text"},"source":["[DataCamp Link](https://www.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas)"]},{"cell_type":"markdown","metadata":{"id":"zkx4zr9DfPWO","colab_type":"text"},"source":["**Course Description**\n","\n","One of the biggest challenges when studying data science technical skills is understanding how those skills and concepts translate into real jobs. Whether you're looking to level up in your marketing job by incorporating Python and pandas or you're trying to get a handle on what kinds of work a data scientist in a marketing organization might do, this course is a great match for you. We'll practice translating common business questions into measurable outcomes, including \"How did this campaign perform?\", \"Which channel is referring the most subscribers?\", \"Why is a particular channel underperforming?\" and more using a fake marketing dataset based on the data of an online subscription business. This course will build on Python and pandas fundamentals, such as merging/slicing datasets, groupby(), correcting data types and visualizing results using matplotlib."]},{"cell_type":"markdown","metadata":{"id":"0tXw-6WLfSRo","colab_type":"text"},"source":["# 1.Pandas\n","\n","In this chapter, you will review pandas basics including importing datasets, exploratory analysis, and basic plotting."]},{"cell_type":"code","metadata":{"id":"GSgvg1ddfZop","colab_type":"code","colab":{}},"source":["# Import pandas into the environment\n","import pandas as pd\n","\n","# Import marketing.csv \n","marketing = pd.read_csv('marketing.csv')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9d8J8z44gJnu","colab_type":"code","outputId":"cfd841a7-0369-4a10-cd54-6af75d4895ad","executionInfo":{"status":"ok","timestamp":1575103801863,"user_tz":-480,"elapsed":888,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":153}},"source":["# Print the first five rows of the DataFrame\n","print(marketing.head())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["      user_id date_served  ... subscribing_channel is_retained\n","0  a100000029      1/1/18  ...           House Ads        True\n","1  a100000030      1/1/18  ...           House Ads        True\n","2  a100000031      1/1/18  ...           House Ads        True\n","3  a100000032      1/1/18  ...           House Ads        True\n","4  a100000033      1/1/18  ...           House Ads        True\n","\n","[5 rows x 12 columns]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gcaTNnovgL6o","colab_type":"code","outputId":"fb91c645-2d24-4e8b-8e8e-f39930ccb1ac","executionInfo":{"status":"ok","timestamp":1575103808953,"user_tz":-480,"elapsed":755,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["# Print the statistics of all columns\n","print(marketing.describe())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["           user_id date_served  ... subscribing_channel is_retained\n","count        10037       10021  ...                1856        1856\n","unique        7309          31  ...                   5           2\n","top     a100000882     1/15/18  ...           Instagram        True\n","freq            12         789  ...                 600        1279\n","\n","[4 rows x 12 columns]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"W5GW_jPTgNe2","colab_type":"code","outputId":"d5ea73d4-a919-48fd-8655-bc70c0be07a5","executionInfo":{"status":"ok","timestamp":1575103815170,"user_tz":-480,"elapsed":756,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":323}},"source":["# Check column data types and non-missing values\n","print(marketing.info())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 10037 entries, 0 to 10036\n","Data columns (total 12 columns):\n","user_id                10037 non-null object\n","date_served            10021 non-null object\n","marketing_channel      10022 non-null object\n","variant                10037 non-null object\n","converted              10022 non-null object\n","language_displayed     10037 non-null object\n","language_preferred     10037 non-null object\n","age_group              10037 non-null object\n","date_subscribed        1856 non-null object\n","date_canceled          577 non-null object\n","subscribing_channel    1856 non-null object\n","is_retained            1856 non-null object\n","dtypes: object(12)\n","memory usage: 941.1+ KB\n","None\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ghN47ixagPl7","colab_type":"text"},"source":["###Data types and data merging\n"]},{"cell_type":"markdown","metadata":{"id":"CgoutChenbyQ","colab_type":"text"},"source":["####Updating the data type of a column\n","\n","- In this exercise, you will modify the data type of the `is_retained` column."]},{"cell_type":"code","metadata":{"id":"MS5cKuJNgQ3f","colab_type":"code","outputId":"35277552-e556-47f1-a046-d06c45f3047a","executionInfo":{"status":"ok","timestamp":1575105919947,"user_tz":-480,"elapsed":738,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Check the data type of is_retained\n","print(marketing['is_retained'].dtype)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["object\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"FmM7jsAInmFq","colab_type":"text"},"source":["- Update the data type of the is_retained column to boolean and then check its data type again."]},{"cell_type":"code","metadata":{"id":"JU3FwIw-nm3y","colab_type":"code","outputId":"91c45827-9103-48aa-cc51-340bbbd35511","executionInfo":{"status":"ok","timestamp":1575105921604,"user_tz":-480,"elapsed":733,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Convert is_retained to a boolean\n","marketing['is_retained'] = marketing['is_retained'].astype('bool')\n","\n","# Check the data type of is_retained, again\n","print(marketing['is_retained'].dtype)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["bool\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5r4JlFdxnqeS","colab_type":"text"},"source":["####Adding new columns\n","\n","In this exercise, you will add two columns to `marketing`:\n","\n","- `day_of_week`: represents the day of the week as an integer (we added a new column, day that represents the day of the week and defined a dictionary that maps the day of the week to each numerical value from 0 to 6 for this purpose.)\n","- `is_correct_lang`: conveys whether the ad was shown to the user in their preferred language"]},{"cell_type":"markdown","metadata":{"id":"A7J6gKIGn0qL","colab_type":"text"},"source":["- Add a new column, `channel_code`, which maps the values in the `subscribing_channel` column to a numeric scale using the `channel_dict` dictionary."]},{"cell_type":"code","metadata":{"id":"MQtwxymSnw-b","colab_type":"code","colab":{}},"source":["# Mapping for channels\n","channel_dict = {\"House Ads\": 1, \"Instagram\": 2, \n","                \"Facebook\": 3, \"Email\": 4, \"Push\": 5}\n","\n","# Map the channel to a channel code\n","marketing['channel_code'] = marketing['subscribing_channel'].map(channel_dict)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"C3nV3JdjoAZ9","colab_type":"text"},"source":["- Add a new column, `is_correct_lang`, which is 'Yes' if the user was shown the ad in their preferred language, 'No' otherwise."]},{"cell_type":"code","metadata":{"id":"M15iEjxRnxWj","colab_type":"code","colab":{}},"source":["# Import numpy\n","import numpy as np\n","\n","# Add the new column is_correct_lang\n","marketing['is_correct_lang'] = np.where(\n","    marketing['language_preferred'] == marketing['language_displayed'], \n","    'Yes', \n","    'No'\n",")"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"449bd8ed-b5d4-4105-adc1-403517f5fecf","executionInfo":{"status":"ok","timestamp":1575105951016,"user_tz":-480,"elapsed":733,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"id":"wqBpUaWroWeH","colab":{"base_uri":"https://localhost:8080/","height":153}},"source":["# Print the first five rows of the DataFrame\n","print(marketing.head())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["      user_id date_served  ... channel_code is_correct_lang\n","0  a100000029      1/1/18  ...          1.0             Yes\n","1  a100000030      1/1/18  ...          1.0             Yes\n","2  a100000031      1/1/18  ...          1.0             Yes\n","3  a100000032      1/1/18  ...          1.0             Yes\n","4  a100000033      1/1/18  ...          1.0             Yes\n","\n","[5 rows x 14 columns]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"d5AME3E2oefR","colab_type":"text"},"source":["####Date columns\n","\n","In this exercise, you will practice reading the CSV with proper date columns and create a day of the week column.\n","\n","- Import pandas into the environment with the alias pd.\n","- Read `'marketing.csv'` into your environment correctly identifying date columns,`'date_served'` `'date_subscribed'` and `'date_canceled'` within your call to `read_csv()`.\n","- Create a day of week column from `'date_subscribed'` using `dt.dayofweek`."]},{"cell_type":"code","metadata":{"id":"lpZY6fQKouyY","colab_type":"code","colab":{}},"source":["# Import pandas into the environment\n","import pandas as pd\n","\n","# Import marketing.csv with date columns\n","marketing = pd.read_csv('marketing.csv', \n","                        parse_dates = ['date_served', 'date_subscribed', 'date_canceled'])\n","\n","# Add a DoW column\n","marketing['DoW'] = marketing['date_subscribed'].dt.dayofweek"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"2X2YkgfIoWZI","colab_type":"code","outputId":"b78b79dc-39ee-4608-e0f4-0014177a4056","executionInfo":{"status":"ok","timestamp":1575106065627,"user_tz":-480,"elapsed":768,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["marketing.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>date_served</th>\n","      <th>marketing_channel</th>\n","      <th>variant</th>\n","      <th>converted</th>\n","      <th>language_displayed</th>\n","      <th>language_preferred</th>\n","      <th>age_group</th>\n","      <th>date_subscribed</th>\n","      <th>date_canceled</th>\n","      <th>subscribing_channel</th>\n","      <th>is_retained</th>\n","      <th>DoW</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>a100000029</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>a100000030</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>19-24 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>a100000031</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>24-30 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>a100000032</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>30-36 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>a100000033</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>36-45 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      user_id date_served  ... is_retained  DoW\n","0  a100000029  2018-01-01  ...        True  0.0\n","1  a100000030  2018-01-01  ...        True  0.0\n","2  a100000031  2018-01-01  ...        True  0.0\n","3  a100000032  2018-01-01  ...        True  0.0\n","4  a100000033  2018-01-01  ...        True  0.0\n","\n","[5 rows x 13 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"cUU3Njieo6Ih","colab_type":"text"},"source":["###Initial exploratory analysis\n"]},{"cell_type":"markdown","metadata":{"id":"Kwnpj8tso8d8","colab_type":"text"},"source":["####Daily marketing reach by channel\n","\n","In this exercise, you'll determine how many users are seeing the marketing assets each day. \n"]},{"cell_type":"markdown","metadata":{"id":"DxxrgNVZpA5d","colab_type":"text"},"source":["- Group the `marketing` DataFrame by `'date_served'` and count the number of unique user IDs."]},{"cell_type":"code","metadata":{"id":"h8wzYGn_pDaN","colab_type":"code","outputId":"8b91a355-5b99-434c-db0a-32d06f7e36d5","executionInfo":{"status":"ok","timestamp":1575106139688,"user_tz":-480,"elapsed":770,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["# Group by date_served and count number of unique user_id's\n","daily_users = marketing.groupby(['date_served'])['user_id'].nunique()\n","\n","# Print head of daily_users\n","print(daily_users.head())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["date_served\n","2018-01-01    362\n","2018-01-02    374\n","2018-01-03    348\n","2018-01-04    323\n","2018-01-05    319\n","Name: user_id, dtype: int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"l_4YZapKpHHe","colab_type":"text"},"source":["####Visualizing daily marketing reach\n","\n","To make it easier for you and your business stakeholders to notice subscriber trends, you will visualize your results using a line plot."]},{"cell_type":"markdown","metadata":{"id":"w2BJWfkdpMWH","colab_type":"text"},"source":["- Use the .plot() method to visualize the results of date_served.\n","- Add the title 'Daily users' and the y-axis label 'Number of users'.\n","- Rotate the x-axis labels by 45 degrees.\n","- Display the plot."]},{"cell_type":"code","metadata":{"id":"9mc1cw6RpDsF","colab_type":"code","outputId":"455ef076-499f-497d-edfc-6f781ac7af59","executionInfo":{"status":"ok","timestamp":1575106314454,"user_tz":-480,"elapsed":804,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Plot daily_subscribers\n","daily_users.plot()\n","\n","# Include a title and y-axis label\n","plt.title('Daily users')\n","plt.ylabel('Number of users')\n","\n","# Rotate the x-axis labels by 45 degrees\n","plt.xticks(rotation = 45)\n","\n","# Display the plot\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAE/CAYAAACpct9bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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mMK3j9qd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SzmcAj4b6jgX3N7Cvh8wKCD/l64G13/xggXCSqv5kNBkqAMndfZWZXhtd4J3x9Xvj6/Dbe\nl8guUVKQnupOYLq7zw/rZX1hB+ekAeXuvl80F3T3S8PWxPHA3LCVYsAV7v6ZpGNmXyD4tt/a34Gv\nAAOBByOnAr9y9//d7vXfjCYmkc7SmIKkuleA6WaWY2b5wInh/nyg1MwygTNbnV8VHiNcq+NjMzsV\ngrpaZjahrV9kZru5+1vu/iNgIzCMoAVyWfh7MLPdzSy3jUs8SNAl9BWCBEH4+gvCVgtmNsTM+rfz\nvkR2iVoKktLcfZ6ZPQjMBzYAs8NDPwTeIvjwfoswERCs6X2bmX2D4MP5TOAWM/sBQUn2B8Jr7cj1\nZjaW4Nv9C+F5C4CRwLywvPtGghlOO4r1vfADfo27l4b7/mlmewBvhl1YW4Gz2nlfIrtEBfFERKSF\nuo9ERKSFuo9EOsnMvg+cut3uv7v7L+IRj0hXUveRiIi0UPeRiIi0UFIQEZEWSgoiItJCSUFERFoo\nKYiISIv/B11gLT4P3qDpAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"7ysBlxyvxfcE","colab_type":"text"},"source":["# 2.Exploratory Analysis & Summary Statistics\n","\n","In this chapter, you will learn about common marketing metrics and how to calculate them using pandas. You will also visualize your results and practice user segmentation."]},{"cell_type":"markdown","metadata":{"id":"2Pnz2E1OpTtf","colab_type":"text"},"source":["###Introduction to common marketing metrics\n"]},{"cell_type":"markdown","metadata":{"id":"pIBWkDI7pcfw","colab_type":"text"},"source":["####Calculating conversion rate\n","\n","On marketing teams, conversion rate is typically the most important metric. It is one of the best ways to determine how effective a marketing team was at gaining new customers.\n","\n","As a reminder, conversion rate is the percentage of the users who saw our marketing assets and subsequently became subscribers.\n","\n","$\\frac{Number of people who convert} {Total number of people who we market to}$"]},{"cell_type":"markdown","metadata":{"id":"b-R3f7pYpfUZ","colab_type":"text"},"source":["- Calculate the number of unique `user_ids` in `marketing` DataFrame.\n","- Calculate the number of people who subscribed using the `converted` column.\n","- Calculate the conversion rate."]},{"cell_type":"code","metadata":{"id":"oqWKZ0kopoCi","colab_type":"code","outputId":"6b2ec8fc-ae2a-4fac-fba3-ceebe7f654b8","executionInfo":{"status":"ok","timestamp":1575106354647,"user_tz":-480,"elapsed":811,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Calculate the number of people we marketed to\n","total = marketing['user_id'].nunique()\n","\n","# Calculate the number of people who subscribed\n","subscribers = marketing[marketing[\"converted\"] == True]\\\n","                            ['user_id'].nunique()\n","\n","# Calculate the conversion rate\n","conversion_rate = subscribers/total\n","print(round(conversion_rate*100, 2), \"%\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["13.89 %\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"S5Fqj64gp8zB","colab_type":"text"},"source":["####Calculating retention rate\n","\n","Conversion rate and retention rate function hand-in-hand; you could create a business with a high conversion rate by giving users a free trial, but have a low retention rate once users are charged for your services.\n","\n","The formula for retention rate is:\n","\n","$\\frac{Number of people who remain subscribed}{Total number of people who converted}$"]},{"cell_type":"markdown","metadata":{"id":"CldsMBL8rD0-","colab_type":"text"},"source":["- Calculate the number of subscribers using the `user_id` and `converted` columns in the `marketing` DataFrame.\n","- Calculate the number of retained subscribers using the boolean columns `user_id` and `is_retained`.\n","- Calculate the retention rate."]},{"cell_type":"code","metadata":{"id":"yZ54RmSfpnsO","colab_type":"code","outputId":"d8401672-3f8c-4e85-d874-e61ad300ec12","executionInfo":{"status":"ok","timestamp":1575106686618,"user_tz":-480,"elapsed":796,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Calculate the number of subscribers\n","total_subscribers = marketing[marketing[\"converted\"] == True]\\\n","                            ['user_id'].nunique()\n","\n","# Calculate the number of people who remained subscribed\n","retained = marketing[marketing['is_retained'] == True]\\\n","                   ['user_id'].nunique()\n","\n","# Calculate the retention rate\n","retention_rate = retained/total_subscribers\n","print(round(retention_rate*100, 2), \"%\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["66.8 %\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"hkbaL8hIrNLh","colab_type":"text"},"source":["###Customer segmentation\n"]},{"cell_type":"markdown","metadata":{"id":"pHHQ07CUrQOu","colab_type":"text"},"source":["####Comparing language conversion rate\n","\n","- Using the `marketing` DataFrame, include only the rows where `language_displayed` is English.\n","- Calculate the total number of users in the `english_speakers` DataFrame.\n","- Calculate the number of conversions in the `english_speakers` DataFrame."]},{"cell_type":"code","metadata":{"id":"c4g6mZRQrKRx","colab_type":"code","outputId":"699eb3af-5bd4-4795-8580-28060559a5b9","executionInfo":{"status":"ok","timestamp":1575107197647,"user_tz":-480,"elapsed":738,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Isolate english speakers\n","english_speakers = marketing[marketing['language_displayed'] == 'English']\n","\n","# Calculate the total number of english speaking users\n","total = english_speakers['user_id'].nunique()\n","\n","# Calculate the number of english speakers who converted\n","subscribers = english_speakers[english_speakers['converted'] == True]\\\n","                            ['user_id'].nunique()\n","\n","# Calculate conversion rate\n","conversion_rate = subscribers/total\n","print('English speaker conversion rate:',  round(conversion_rate*100,2), '%')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["English speaker conversion rate: 12.92 %\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"VUpF2xfWtS--","colab_type":"text"},"source":["- Group the `marketing` DataFrame by `language_displayed` and count the number of unique users in the dataset.\n","- Group `marketing` by `language_displayed` again, this time calculating the number of unique users who converted.\n","- Calculate the conversion rate for all languages."]},{"cell_type":"code","metadata":{"id":"hy2ktZnlrKII","colab_type":"code","outputId":"4dda7381-815f-427c-eca6-0ae26883d58f","executionInfo":{"status":"ok","timestamp":1575107332588,"user_tz":-480,"elapsed":738,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["# Group by language_displayed and count unique users\n","total = marketing.groupby(['language_displayed'])\\\n","                                  ['user_id'].nunique()\n","\n","# Group by language_displayed and count unique conversions\n","subscribers = marketing[marketing['converted'] == True]\\\n","                       .groupby(['language_displayed'])\\\n","                       ['user_id'].nunique()\n","\n","# Calculate the conversion rate for all languages\n","language_conversion_rate = subscribers/total\n","print(language_conversion_rate)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["language_displayed\n","Arabic     0.500000\n","English    0.129167\n","German     0.716216\n","Spanish    0.200000\n","Name: user_id, dtype: float64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"J1YwbaRAtd5G","colab_type":"text"},"source":["####Aggregating by date\n","\n","- Group the `marketing` DataFrame by `date_served` and count the number of unique users per day.\n","- Isolate converted users in `marketing` while grouping by `date_served` and counting the number of unique converted users per day."]},{"cell_type":"code","metadata":{"id":"2ptD_jWstbdJ","colab_type":"code","outputId":"acffbeab-232b-41b1-b510-97d96f966556","executionInfo":{"status":"ok","timestamp":1575107341342,"user_tz":-480,"elapsed":790,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":578}},"source":["# Group by date_served and count unique users\n","total = marketing.groupby(['date_served'])['user_id']\\\n","                     .nunique()\n","\n","# Group by date_served and count unique converted users\n","subscribers = marketing[marketing['converted'] == True]\\\n","                         .groupby(['date_served'])\\\n","                         ['user_id'].nunique()\n","\n","# Calculate the conversion rate per day\n","daily_conversion_rate = subscribers/total\n","print(daily_conversion_rate)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["date_served\n","2018-01-01    0.099448\n","2018-01-02    0.098930\n","2018-01-03    0.103448\n","2018-01-04    0.108359\n","2018-01-05    0.125392\n","2018-01-06    0.113636\n","2018-01-07    0.141818\n","2018-01-08    0.115385\n","2018-01-09    0.125000\n","2018-01-10    0.118694\n","2018-01-11    0.080645\n","2018-01-12    0.076412\n","2018-01-13    0.084967\n","2018-01-14    0.085246\n","2018-01-15    0.113429\n","2018-01-16    0.255155\n","2018-01-17    0.219512\n","2018-01-18    0.091195\n","2018-01-19    0.059016\n","2018-01-20    0.067524\n","2018-01-21    0.087336\n","2018-01-22    0.123596\n","2018-01-23    0.122093\n","2018-01-24    0.115789\n","2018-01-25    0.125000\n","2018-01-26    0.090090\n","2018-01-27    0.065625\n","2018-01-28    0.062500\n","2018-01-29    0.059561\n","2018-01-30    0.066246\n","2018-01-31    0.052941\n","Name: user_id, dtype: float64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8G2YS0X9t-Ja","colab_type":"text"},"source":["###Plotting campaign results (I)\n"]},{"cell_type":"markdown","metadata":{"id":"PwCBev2OuBTT","colab_type":"text"},"source":["####Visualize conversion rate by language"]},{"cell_type":"markdown","metadata":{"id":"3Gg2gjWauEzr","colab_type":"text"},"source":["- Create a bar chart using the `language_conversion_rate` DataFrame.\n","- Add the title `'Conversion rate by language\\n'` to your chart with font size 16.\n","- Add an x-axis label, `'Language'`, and a y-axis label, `'Conversion rate (%)'`, both with font size 14.\n","- Display the plot."]},{"cell_type":"code","metadata":{"id":"nDXBHKpHtbSD","colab_type":"code","outputId":"bdf06ab9-3829-4119-c132-7b5cb15b43e3","executionInfo":{"status":"ok","timestamp":1575107487881,"user_tz":-480,"elapsed":787,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":349}},"source":["# Create a bar chart using language_conversion_rate DataFrame\n","language_conversion_rate.plot(kind = 'bar')\n","\n","# Add a title and x and y-axis labels\n","plt.title('Conversion rate by language\\n', size = 16)\n","plt.ylabel('Conversion rate (%)', size = 14)\n","plt.xlabel('Language', size = 14)\n","\n","# Display the plot\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYYAAAFMCAYAAADP8xs3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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OA1YFrhC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\np/gAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"9dSAT0gzuQ4l","colab_type":"text"},"source":["####Creating daily conversion rate DataFrame\n"]},{"cell_type":"markdown","metadata":{"id":"r9oKqvEOuTed","colab_type":"text"},"source":["- Group `marketing` by `'date_served'` and calculate the unique number of user IDs.\n","- Select only the rows in marketing where `converted` equals `True`. Group the result by `'date_served'` and calculate the unique number of user IDs."]},{"cell_type":"code","metadata":{"id":"ISWFkyxvuYfW","colab_type":"code","colab":{}},"source":["# Group by date_served and count unique users\n","total = marketing.groupby(['date_served'])['user_id']\\\n","                     .nunique()\n","\n","# Group by date_served and calculate subscribers\n","subscribers = marketing[marketing['converted'] == True]\\\n","                         .groupby(['date_served'])\\\n","                         ['user_id'].nunique()\n","\n","# Calculate the conversion rate for all languages\n","daily_conversion_rate = subscribers/total"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AIznYIITueO2","colab_type":"text"},"source":["####Setting up our data to visualize daily conversion"]},{"cell_type":"markdown","metadata":{"id":"6I1AqSwwugMo","colab_type":"text"},"source":["- Reset the index of `daily_conversion_rate` and use `pd.DataFrame` to convert the results into a DataFrame.\n","- Rename the `daily_conversion_rate` columns to `'date_served'` and `'conversion_rate'`."]},{"cell_type":"code","metadata":{"id":"Rvq1Sr1NuYw2","colab_type":"code","colab":{}},"source":["# Reset index to turn the results into a DataFrame\n","daily_conversion_rate = pd.DataFrame(daily_conversion_rate.reset_index(0))\n","\n","# Rename columns\n","daily_conversion_rate.columns = ['date_served', \n","                                 'conversion_rate']"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"bGsJoxKhunVy","colab_type":"code","outputId":"b852a232-e4e1-4ee7-8b1f-4518cd3b6bc6","executionInfo":{"status":"ok","timestamp":1575107603676,"user_tz":-480,"elapsed":812,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["daily_conversion_rate.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>date_served</th>\n","      <th>conversion_rate</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2018-01-01</td>\n","      <td>0.099448</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2018-01-02</td>\n","      <td>0.098930</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2018-01-03</td>\n","      <td>0.103448</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-01-04</td>\n","      <td>0.108359</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-01-05</td>\n","      <td>0.125392</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  date_served  conversion_rate\n","0  2018-01-01         0.099448\n","1  2018-01-02         0.098930\n","2  2018-01-03         0.103448\n","3  2018-01-04         0.108359\n","4  2018-01-05         0.125392"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"xzlwPmeZusko","colab_type":"text"},"source":["####Visualize daily conversion rate\n"]},{"cell_type":"markdown","metadata":{"id":"jrW2Dsa5utng","colab_type":"text"},"source":["- Create a line chart using the daily_conversion_rate DataFrame.\n","- Set the y-axis of your chart to begin at 0.\n","- Display the chart."]},{"cell_type":"code","metadata":{"id":"ojT3MsuNuweo","colab_type":"code","outputId":"087cbf1b-ffc7-4c77-e494-1309088399ad","executionInfo":{"status":"ok","timestamp":1575107684033,"user_tz":-480,"elapsed":850,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":354}},"source":["# Create a line chart using daily_conversion_rate DataFrame\n","daily_conversion_rate.plot('date_served', #'date_subscribed', \n","                           'conversion_rate')\n","\n","plt.title('Daily conversion rate\\n', size = 16)\n","plt.ylabel('Conversion rate (%)', size = 14)\n","plt.xlabel('Date', size = 14)\n","\n","# Set the y-axis to begin at 0\n","plt.ylim(0)\n","\n","# Display the plot\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYwAAAFRCAYAAABnmZq7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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faVq4knC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emD/XqtUvKKrjohe85kFvEl38aEdSTMvrDLe+lsXDjAZb8ZXTVYlaNVW0Jw5OvYK8CA7FW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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"ACIZhRz4vAfD","colab_type":"text"},"source":["####Plotting campaign results (II)\n"]},{"cell_type":"markdown","metadata":{"id":"JP5lBZO-vCXD","colab_type":"text"},"source":["####Marketing channels across age groups\n","\n","In this exercise, you will create a grouped bar chart showing how many people each marketing channel reached by age group."]},{"cell_type":"markdown","metadata":{"id":"5mvCntFYvIqM","colab_type":"text"},"source":["- Unstack `channel_age` with `level = 1` and transform the result into a DataFrame.\n","- Plot `channel_age` as a grouped bar chart.\n","- Add a legend in the upper right and set the labels equal to `channel_age_df.columns.values`."]},{"cell_type":"code","metadata":{"id":"9Y5ZvIFqvklf","colab_type":"code","outputId":"853f1fe3-e2e8-4ac0-b602-046ee43249b2","executionInfo":{"status":"ok","timestamp":1575107844799,"user_tz":-480,"elapsed":807,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["marketing.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>date_served</th>\n","      <th>marketing_channel</th>\n","      <th>variant</th>\n","      <th>converted</th>\n","      <th>language_displayed</th>\n","      <th>language_preferred</th>\n","      <th>age_group</th>\n","      <th>date_subscribed</th>\n","      <th>date_canceled</th>\n","      <th>subscribing_channel</th>\n","      <th>is_retained</th>\n","      <th>DoW</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>a100000029</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>a100000030</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>19-24 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>a100000031</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>24-30 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>a100000032</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>30-36 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>a100000033</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>36-45 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      user_id date_served  ... is_retained  DoW\n","0  a100000029  2018-01-01  ...        True  0.0\n","1  a100000030  2018-01-01  ...        True  0.0\n","2  a100000031  2018-01-01  ...        True  0.0\n","3  a100000032  2018-01-01  ...        True  0.0\n","4  a100000033  2018-01-01  ...        True  0.0\n","\n","[5 rows x 13 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"mzHLAXW3vtux","colab_type":"code","colab":{}},"source":["channel_age = marketing.groupby(['marketing_channel', 'age_group'])\\\n","                                ['user_id'].count()\n","\n","# Unstack channel_age and transform it into a DataFrame\n","channel_age_df = pd.DataFrame(channel_age.unstack(level = 1))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hmsJdKYWvwCI","colab_type":"code","outputId":"6980f054-f9cf-4616-9762-73360a45a1ec","executionInfo":{"status":"ok","timestamp":1575107893816,"user_tz":-480,"elapsed":735,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":252}},"source":["channel_age_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>age_group</th>\n","      <th>0-18 years</th>\n","      <th>19-24 years</th>\n","      <th>24-30 years</th>\n","      <th>30-36 years</th>\n","      <th>36-45 years</th>\n","      <th>45-55 years</th>\n","      <th>55+ years</th>\n","    </tr>\n","    <tr>\n","      <th>marketing_channel</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Email</th>\n","      <td>93</td>\n","      <td>111</td>\n","      <td>120</td>\n","      <td>60</td>\n","      <td>63</td>\n","      <td>65</td>\n","      <td>53</td>\n","    </tr>\n","    <tr>\n","      <th>Facebook</th>\n","      <td>264</td>\n","      <td>347</td>\n","      <td>296</td>\n","      <td>249</td>\n","      <td>226</td>\n","      <td>299</td>\n","      <td>179</td>\n","    </tr>\n","    <tr>\n","      <th>House Ads</th>\n","      <td>694</td>\n","      <td>761</td>\n","      <td>681</td>\n","      <td>699</td>\n","      <td>589</td>\n","      <td>678</td>\n","      <td>631</td>\n","    </tr>\n","    <tr>\n","      <th>Instagram</th>\n","      <td>300</td>\n","      <td>315</td>\n","      <td>286</td>\n","      <td>259</td>\n","      <td>280</td>\n","      <td>227</td>\n","      <td>204</td>\n","    </tr>\n","    <tr>\n","      <th>Push</th>\n","      <td>186</td>\n","      <td>145</td>\n","      <td>183</td>\n","      <td>86</td>\n","      <td>193</td>\n","      <td>82</td>\n","      <td>118</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["age_group          0-18 years  19-24 years  ...  45-55 years  55+ years\n","marketing_channel                           ...                        \n","Email                      93          111  ...           65         53\n","Facebook                  264          347  ...          299        179\n","House Ads                 694          761  ...          678        631\n","Instagram                 300          315  ...          227        204\n","Push                      186          145  ...           82        118\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"OCqUvOEUunM_","colab_type":"code","outputId":"64933d2b-6425-4f00-d0a4-dd7bb17a2f69","executionInfo":{"status":"ok","timestamp":1575108088874,"user_tz":-480,"elapsed":1671,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":338}},"source":["# Plot the results\n","channel_age_df.plot(kind = 'bar')\n","plt.title('Marketing channels by age group')\n","plt.xlabel('Age Group')\n","plt.ylabel('Users')\n","# Add a legend to the plot\n","plt.legend(loc = 'upper right', \n","           labels = channel_age_df.columns.values)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAFBCAYAAACYWrPqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI0aMoFmzZqSlpdGhQwemTZvGo48+yqBBgx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XKlU6iT0tL44cffmDt2rU899xz9OvXL8eQ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\nRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"Xth6ckQGwr5A","colab_type":"text"},"source":["####Grouping and counting by multiple columns\n"]},{"cell_type":"markdown","metadata":{"id":"c97HbzG2wthZ","colab_type":"text"},"source":["- Use `.groupby()` to calculate subscribers by `subscribing_channel` and `date_subscribed`."]},{"cell_type":"code","metadata":{"id":"Dtqh9yGkvRdZ","colab_type":"code","outputId":"4aadf215-18cf-4c15-ff94-ae7aae7f5173","executionInfo":{"status":"ok","timestamp":1575108181362,"user_tz":-480,"elapsed":761,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["# Count the subs by subscribing channel and date subscribed\n","retention_total = marketing.groupby(['date_subscribed',\n","                                     'subscribing_channel'])\\\n","                                       ['user_id'].nunique()\n","\n","# Print results\n","print(retention_total.head())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["date_subscribed  subscribing_channel\n","2018-01-01       Email                   1\n","                 Facebook                8\n","                 House Ads              16\n","                 Instagram               8\n","                 Push                    3\n","Name: user_id, dtype: int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"aJGRx6f7w17x","colab_type":"text"},"source":["- Use `.groupby()` to calculate retained subscribers by `subscribing_channel` and `date_subscribed`."]},{"cell_type":"code","metadata":{"id":"eopcIKqyxAGO","colab_type":"code","outputId":"eb99d18f-0793-44a8-a400-07fc3c4d3ed8","executionInfo":{"status":"ok","timestamp":1575108221555,"user_tz":-480,"elapsed":761,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["# Count the retained subs by subscribing channel and date subscribed\n","retention_subs = marketing[marketing['is_retained'] == True]\\\n","                           .groupby(['date_subscribed',\n","                                     'subscribing_channel'])\\\n","                            ['user_id'].nunique()\n","\n","# Print results\n","print(retention_subs.head())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["date_subscribed  subscribing_channel\n","2018-01-01       Email                   1\n","                 Facebook                7\n","                 House Ads              11\n","                 Instagram               6\n","                 Push                    3\n","Name: user_id, dtype: int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8B757grxxsD7","colab_type":"text"},"source":["####Analyzing retention rates for the campaign\n","\n","In this exercise, you will calculate the retention rate for each subscription channel and visualize your results."]},{"cell_type":"markdown","metadata":{"id":"BkhID1n4xuff","colab_type":"text"},"source":["- Divide `retention_subs` by `retention_total` to get the retention rate by subscribing channel by date subscribed.\n","- Add the title `'Retention Rate by Subscribing Channel'`.\n","- Name the x-axis label and y-axis label `'Date Subscribed'` and `'Retention Rate (%)'` respectively.\n","- Place the legend in the `upper right` and display the plot."]},{"cell_type":"code","metadata":{"id":"uJ3cWhgWfc-h","colab_type":"code","outputId":"c49f5bcd-f333-4455-964b-94dc893bd751","executionInfo":{"status":"ok","timestamp":1575108461727,"user_tz":-480,"elapsed":1371,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":330}},"source":["# Divide retained subscribers by total subscribers\n","retention_rate = retention_subs/retention_total\n","retention_rate_df = pd.DataFrame(retention_rate.unstack(level=1))\n","\n","# Plot retention rate\n","retention_rate_df.plot()\n","\n","# Add a title, x-label, y-label, legend and display the plot\n","plt.title('Retention Rate by Subscribing Channel')\n","plt.xlabel('Date Subscribed')\n","plt.ylabel('Retention Rate (%)')\n","plt.legend(loc = 'upper right', \n","           labels = retention_rate_df.columns.values)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAE5CAYAAACd9yc/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDdUqyWWgOzllRBBLmwwmDa46Zw7vP3X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geGGKseCoMhyJAChpUJnHkJhBFz63jtelZT1y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\n/X4jGy+YiRUAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"hXQfBrT_fdRx","colab_type":"text"},"source":["# 3. Conversion Attribution\n","\n","In this chapter, you will build functions to automate common marketing analysis and determine why certain marketing channels saw lower than usual conversion rates during late January."]},{"cell_type":"markdown","metadata":{"id":"nzcfjqQSyAW6","colab_type":"text"},"source":["###Building functions to automate analysis\n"]},{"cell_type":"markdown","metadata":{"id":"xKNzpDhYyCYp","colab_type":"text"},"source":["####Building a conversion function\n","\n","In this exercise, you will build a function that takes a DataFrame and list of column names and outputs the conversion rate across the column(s)."]},{"cell_type":"markdown","metadata":{"id":"BCnJwBacyI_C","colab_type":"text"},"source":["- Isolate rows in the user inputted `dataframe` where users were converted, then group by the list of user inputted `column_names` and count the number of unique converted users.\n","- Group the user inputted `dataframe` by the list of user inputted `column_names` and calculate the total number of users.\n","- Fill any missing values in `conversion_rate` with `0`."]},{"cell_type":"code","metadata":{"id":"NYjgatkcyO24","colab_type":"code","colab":{}},"source":["def conversion_rate(dataframe, column_names):\n","    # Total number of converted users\n","    column_conv = dataframe[dataframe['converted'] == True]\\\n","                       .groupby(column_names)['user_id'].nunique()\n","\n","    # Total number users\n","    column_total = dataframe.groupby(column_names)['user_id'].nunique()   \n","    \n","    # Conversion rate \n","    conversion_rate = column_conv/column_total\n","    \n","    # Fill missing values with 0\n","    conversion_rate = conversion_rate.fillna(0)\n","    return conversion_rate"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"i3qf_2XLyVS8","colab_type":"text"},"source":["####Test and visualize conversion function\n","\n","- Use your `conversion_rate()` function to calculate the conversion rate in `marketing` by `date_served` and age_group and store your results in `age_group_conv`.\n","- Unstack `age_group_conv` at level equal to 1 and wrap that in a call to `pd.DataFrame()` to create `age_group_df`.\n","- Create a line chart to display your results from `age_group_df`."]},{"cell_type":"code","metadata":{"id":"8dLYhMWvyjyo","colab_type":"code","outputId":"da745424-b650-4be1-abfe-c832842e8a6d","executionInfo":{"status":"ok","timestamp":1575108629365,"user_tz":-480,"elapsed":733,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["marketing.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>date_served</th>\n","      <th>marketing_channel</th>\n","      <th>variant</th>\n","      <th>converted</th>\n","      <th>language_displayed</th>\n","      <th>language_preferred</th>\n","      <th>age_group</th>\n","      <th>date_subscribed</th>\n","      <th>date_canceled</th>\n","      <th>subscribing_channel</th>\n","      <th>is_retained</th>\n","      <th>DoW</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>a100000029</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>a100000030</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>19-24 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>a100000031</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>24-30 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>a100000032</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>30-36 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>a100000033</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>36-45 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaT</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      user_id date_served  ... is_retained  DoW\n","0  a100000029  2018-01-01  ...        True  0.0\n","1  a100000030  2018-01-01  ...        True  0.0\n","2  a100000031  2018-01-01  ...        True  0.0\n","3  a100000032  2018-01-01  ...        True  0.0\n","4  a100000033  2018-01-01  ...        True  0.0\n","\n","[5 rows x 13 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"code","metadata":{"id":"-XEz4Bbxfh4x","colab_type":"code","outputId":"31d22edf-a2c0-4781-8416-0e8122bb72db","executionInfo":{"status":"ok","timestamp":1575108633428,"user_tz":-480,"elapsed":1590,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":354}},"source":["# Calculate conversion rate by age_group\n","age_group_conv = conversion_rate(marketing, ['date_served',\n","                                             'age_group'])\n","\n","# Unstack and create a DataFrame\n","age_group_df = pd.DataFrame(age_group_conv.unstack(level = 1))\n","\n","# Visualize conversion by age_group\n","age_group_df.plot()\n","plt.title('Conversion rate by age group\\n', size = 16)\n","plt.ylabel('Conversion rate', size = 14)\n","plt.xlabel('Age group', size = 14)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYwAAAFRCAYAAABnmZq7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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YgIANu2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\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hKZTePC8bkg5D3WBwccXf359Vq1aBshqRqJ71yhyvwCIAowaFM3UorFYrW7duZdmyZYXa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\nA9YuIgseZEG4AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"8SdFvY7gyoc3","colab_type":"text"},"source":["####Plotting function\n","\n","In this exercise, you will build a function to plot the results of your conversion rate function."]},{"cell_type":"markdown","metadata":{"id":"yues78JIyqn4","colab_type":"text"},"source":["- Create a for loop for each `column` in the `dataframe`.\n","- Plot a line chart of the column by the DataFrame's index.\n","- Show the plot."]},{"cell_type":"code","metadata":{"id":"IncrVLpuyikW","colab_type":"code","colab":{}},"source":["def plotting_conv(dataframe):\n","    for column in dataframe:\n","        # Plot column by dataframe's index\n","        plt.plot(dataframe.index, dataframe[column])\n","        plt.title('Daily ' + str(column) + ' conversion rate\\n', \n","                  size = 16)\n","        plt.ylabel('Conversion rate', size = 14)\n","        plt.xlabel('Date', size = 14)\n","        # Show plot\n","        plt.show()\n","        plt.clf()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4dgga4g6yxs4","colab_type":"text"},"source":["####Putting it all together\n"]},{"cell_type":"markdown","metadata":{"id":"cY0wS2mGyz0E","colab_type":"text"},"source":["- Using your `conversion_rate()` function, create a new DataFrame called `age_group_conv` which contains conversion rate by `date_served` and `age_group` from the marketing DataFrame.\n","- Unstack `age_group_conv` to create a DataFrame with each age group as a column. This step has already been done for you.\n","- Use your `plotting_conv()` function to plot the conversion rates for each age group."]},{"cell_type":"code","metadata":{"id":"IHDBZiCLyia3","colab_type":"code","colab":{}},"source":["# Calculate conversion rate by date served and age group\n","age_group_conv = conversion_rate(marketing, \n","                                 ['date_served', 'age_group'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"fzwIWjylzXHm","colab_type":"code","outputId":"8da5e185-c088-4087-b4ba-1328c078c16e","executionInfo":{"status":"ok","timestamp":1575108844362,"user_tz":-480,"elapsed":701,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["age_group_conv.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["date_served  age_group  \n","2018-01-01   0-18 years     0.155172\n","             19-24 years    0.196721\n","             24-30 years    0.105263\n","             30-36 years    0.040816\n","             36-45 years    0.042553\n","Name: user_id, dtype: float64"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"id":"7RtedoLmzUb1","colab_type":"code","colab":{}},"source":["# Unstack age_group_conv and create a DataFrame\n","age_group_df = pd.DataFrame(age_group_conv.unstack(level=1))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9L8il2oHzNKj","colab_type":"code","outputId":"e3fb689b-2b69-49a1-eb5e-d82be3d2160c","executionInfo":{"status":"ok","timestamp":1575108853386,"user_tz":-480,"elapsed":735,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":252}},"source":["age_group_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>age_group</th>\n","      <th>0-18 years</th>\n","      <th>19-24 years</th>\n","      <th>24-30 years</th>\n","      <th>30-36 years</th>\n","      <th>36-45 years</th>\n","      <th>45-55 years</th>\n","      <th>55+ years</th>\n","    </tr>\n","    <tr>\n","      <th>date_served</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-01-01</th>\n","      <td>0.155172</td>\n","      <td>0.196721</td>\n","      <td>0.105263</td>\n","      <td>0.040816</td>\n","      <td>0.042553</td>\n","      <td>0.022222</td>\n","      <td>0.086957</td>\n","    </tr>\n","    <tr>\n","      <th>2018-01-02</th>\n","      <td>0.180328</td>\n","      <td>0.164384</td>\n","      <td>0.114754</td>\n","      <td>0.040000</td>\n","      <td>0.043478</td>\n","      <td>0.043478</td>\n","      <td>0.024390</td>\n","    </tr>\n","    <tr>\n","      <th>2018-01-03</th>\n","      <td>0.102041</td>\n","      <td>0.208955</td>\n","      <td>0.150943</td>\n","      <td>0.042553</td>\n","      <td>0.060000</td>\n","      <td>0.047619</td>\n","      <td>0.043478</td>\n","    </tr>\n","    <tr>\n","      <th>2018-01-04</th>\n","      <td>0.117647</td>\n","      <td>0.200000</td>\n","      <td>0.183673</td>\n","      <td>0.045455</td>\n","      <td>0.043478</td>\n","      <td>0.061224</td>\n","      <td>0.073171</td>\n","    </tr>\n","    <tr>\n","      <th>2018-01-05</th>\n","      <td>0.142857</td>\n","      <td>0.250000</td>\n","      <td>0.136364</td>\n","      <td>0.090909</td>\n","      <td>0.068182</td>\n","      <td>0.046512</td>\n","      <td>0.088889</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["age_group    0-18 years  19-24 years  ...  45-55 years  55+ years\n","date_served                           ...                        \n","2018-01-01     0.155172     0.196721  ...     0.022222   0.086957\n","2018-01-02     0.180328     0.164384  ...     0.043478   0.024390\n","2018-01-03     0.102041     0.208955  ...     0.047619   0.043478\n","2018-01-04     0.117647     0.200000  ...     0.061224   0.073171\n","2018-01-05     0.142857     0.250000  ...     0.046512   0.088889\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"id":"FGZSPf1dzLxa","colab_type":"code","outputId":"4d9096ab-bd65-44f4-f6ce-5e9778fc648b","executionInfo":{"status":"ok","timestamp":1575108957026,"user_tz":-480,"elapsed":2568,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Plot the results\n","plotting_conv(age_group_df)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaQAAAEuCAYAAADBZ2aLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRISwBiQROVtE1orIBhG5rYX93xWR5SKyVETmishY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DwlgeF4Gmy0g9RheA9KjwP0i8h0R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\naNehWnaqOldVvw8MAC4DRgMhq5oQjSYOzmJ5UQVNIUjLnb12N68uLuJ7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XiikJsWTnhQf\n8h5STX0T/15azLnjB9ArJbHD54sIvzx/LOf86WO+/rfPeOTrk9ucZ6eqFJXX8KUj287kiwQiwrRR\nuby6uIj6xuYur67cFt9cOYDahkOHP5MS4hjcJ5Uh2Wnk90kjPzuVUf0yOXV05P8MA7X5ExSRc0Rk\ni4gcNrYkIlnuPq9ZdscBG1R1k6rW45QgutD/AFXdoqrLgMAB57OAd1W11A1C7wJne7xvt0pKiOOk\nETkcOaAXL94w1YKRn+4atvvnZ9soq27gxtPaXnwv0uVmhr580H9W7GBfXWOHh+v8jeyXyV+vmcKO\nihq+8ue5zFrZ+u97b1U9dY3NUfP/ZPqovlTXN7Fwa2lI77OpZD+1Dc3cM+Mo1tx9Nu//ZDpPffNY\n7r5wHNeeOJTR/TIp2VfH60uL+M3ba3jw/fUhbU+otNdDugn4vaoetiqsqlaIyL04C/Z56TUNAvzX\nQygEjvfYzpbOjY4p9cBjV08hMV6i7sF5qPmy7R6bs4npo/p2uphsR9Q2NPH4x5s4cXgOxxzRJ+T3\nC6Vw1LN7ccF2jshJ44RhXXvOdtrovrz5g5P5/j8Wc8Ozi7h+2jD+56zRh01ELg7hHKRQmDo8h4Q4\nYc66Ek4cHrr1xFYUO1NHxg/KIiUxnuF5GQzPa3nYv6K6gcra6Fwlt70+5gTgvTb2fwBMDF5zukZE\nrheRhSKysKQkch40JiXEWTBqgYhw78UT2FVZy8WPzGNTSeiX3HppUSEl++q4Kcp7R+BMjg1l+aAt\ne6r4bHMpl07JD8rf7+A+abz03alcdcIQHv9oE1f+9TN2Vx66flIw1kEKp4zkBKYM7cOctaF9vVlR\nVElyQhzD89ovK5aVlkh+dlpI2xMq7QWkPA4fPvOnQE4b+/0VAf79/sHutqCdq6qPq+oUVZ2Slxdd\n80pi1dThObxw/VRqG5q45NH5XVr2vT0NTc08Onsjk4f0Zupwr3+2kSsnxD2klxZtJ07g4smdL5Ia\nKDkhnl/POIr/u2wSy4sqOPfBuXy66WC1g2AsXR5u00blsWbnPnZVBmdxwpasKKrgyAG9oqa0VWe1\n990V4vSSWjMB70FlATBSRApEJAm4HHjD47mzgDNFpI+I9MFZm2mWx3NNhDtqcBYvf+9E0pLiueLx\nT5m7fk9I7vOvJUUUlddw0+kjekSPNTcjmdLqeho7MMfHq8amZl5eVMj0UXkhGUqdcfQgXr/pJHql\nJnDlXz/lkdkbUVWKy2tJS4qnd1rHEyi6y3Rf+neIsu2am5VVxZWMH9Tzp4m0F5DeAu4WkcPerohI\nGnCXe0y7VLUR55nULGA1MFNVV4rIXSJygXvNY0WkEPga8JiIrHTPLQXuxglqC4C73G2mhyjITeeV\n751IfnYa33zqc95cVhzU6zc1K4/M3sjYAb04LQqzj1qSl5GEKpRWB3/Y7qP1JeyqrPNcmaEzRvXL\n5I2bTuacowZw7ztruO6ZRazdVcnAICzMF05H9u9FbkYyH4XojdS20mr21TUyfmB0ZoR2RHtJDfcA\nlwDrROQhwLc+8ZE4wUWA33i9maq+DbwdsO2Xfp8vwBmOa+ncJ4Envd7LRJ9+vVJ48YapfOfpBfzg\n+SWUVTdw9QlHBOXaby/fwaY9Vfzl65Oj6sWuLQfLB9XTNzO4vZiZCwrJSU/i9DGhLfibkZzAQ1cc\nzTFD+vCbt1fT2KwHJpxGi7g4J/37gzW7aWrWDlV78GJ50cGEhp6uzR6Squ4GTgSW4wSe19yPe4Bl\nwMmquivUjTSxIys1kWe/fTxfGtOXX/xrBQ+8u67LS0WrKg9/uIHheemcPa5/kFra/XJ8k2OD/Bxp\nz/463lu9i68ePSikc2t8RIRvnVzAizdMZWhOGsdGYfbj9FF5lFc3HAgewbSiuILEeGFUv8ygXzvS\ntDsxVlW3Aue6z25G4PSK1kfDpFQTnVIS43n0qmO47dXl/On99ZRW1XPHBeM6/c7z/dW7WbNzH3/4\n2sSoqujdntwQ1bN7eVEhjc3eC6kGyzFH9GH2LaeF9Z7BcsrIPERgztqSoNenXFlU6UywD8Obg+7m\n+TtU1TJVXaCqn1swMqGWEB/H7y+ZwA3Th/Hsp1v54QtLqGvs+No/qsqfP9xAfnZqp0rfRLLczIND\ndsHS1Kz847OtHF+QzcgYeEceLNnpSUwYlBX0unaqyoriiph4fgQdXMLcmHASEX52zpH8/NwxvLVs\nB9/8+wIqqjs24e+TDXv5Yns5350+nMQeljKbmZxAUnxcUFeO/WhdCdtLa7h6anCe3cWSaaPyWLKt\nrMN/o20pKq+hvLqBcTHw/AgsIJkocP204fzx0oks2FLKVx/5hM0dKPf/0Ifr6dcrmUuOCd5cmkgh\nIuRmJAW1nt0z87eQl5nMmWN7zrO2cJk+Ko9mdZZ6D5YVRU6RnPEDe37KN1hAMlHiosmD+cd3TqCs\nqp4ZD3/CvI3t/6dfuKWUTzeVcv204WFdWyecglnPbtveamavK+GKY/Nj4nlFsE3K701mSkJQ5yOt\nLK4gPk5iZqka+6szUeO4gmxev/Fk8jKT+cYTn/P859vaPP6hDzeQnZ7EFceF9+F8OOVmJLM3SEtQ\n/OPzrcSJcMXxQ4JyvViTEB/HySNymbOupMuZoT4riioYkZdBSmLPfEMVyAKSiSpDctJ49fsncuKI\nXH726nLufnMVTS2sNOQHPgAAEzVJREFUB7OiqILZa0v49skFpCV5XmUl6uSkB2fIrrahiZkLtvPl\nI/sxICt6yvZEmmmj8thZWcv63cGpy7iiuJJxMVChwccCkok6vVISefKaKVx74lCemLuZ655ZyL6A\n6sYPfbCBzJSEHv9wPjfT6SF19R3528t3OBORe/jPK9R8k3qDUWx1d2UtJfvqYibDDiwgmSiVEB/H\nHReM4+4Z45mzroRLHpnP9tJqANbv2sc7K3dy7YlDO7WoXDTJzUimoUmprGns0nWemb+VYXnpnNgD\nis52p0G9UxnRNyMo6d/+S07ECgtIJqpdfcIRPPXNYymuqGHGw5+waGspf5m9kdTEeL55UkF3Ny/k\nfJNju5L6vbywgqXby7nq+CN6TFml7jR9VB6fbSqlur5rbxJWFFUiAmNjJMMOLCCZHuCUkXm89v2T\nyEhJ4IrHP+P1pUVcdcIQssO4LHp3yQ1C+aDnPt1KamI8F/fA1PjuMG1UHvVNzXy2qWv1n1cUVVCQ\nm05Gcs99BhrIApLpEUb0zeBf3z+JyUf0Ji0pgetOGdbdTQqLrgakiuoGXv+iiBlHDyQrtWcPb4bL\n8QXZJCfEMaeL6d8riytj6vkReKhlZ0y06JOexD+/cwL7ahvJiqL1dLrCN2TX2fJBLy8upLahmauC\nVFXdOLUYTxiW06X5SKVV9RSV13DNibH1e7EekulR4uIkZoIRQO+0JOKkcz2k5mbluU+3MnlIb8bF\n2DvxUJs2Ko9Ne6oOJNp01EpfQkOM/V4sIBkTxeLjhOz0zlVr+GTjHjbvqeIbU4cGv2Ex7tTRTvr3\nu6s6tzqPbxmLWHujYAHJmCiXm5FESScmxz47fyvZ6Umcc5TVrQu24XkZHDmgF68vLerU+SuLKsnP\nTo2p3j5YQDIm6uVldrx8UHF5De+t3sVlx+b32Dp/3W3GpIF8UVjRoWLAPrG05IQ/C0jGRLm8jGQ2\n7N7P0u3lns95/vNtKHDlcVa3LlQumDQQEfjXko71kipqGti6tzqmJsT6WEAyJsp9+5QCMpMTuPiR\nefzx3XU0NDW3eXx9YzPPf76dL43pS352WphaGXsGZKVyfEE2ry8t6lBpp1XFzpIT42JoQqyPBSRj\noty4gVm88+NpXDhpIA++v56v/uUT1u/a1+rx76zcyZ79dZbqHQYzJg1iy95qviis8HyOL8Mu1hIa\nwAKSMT1Cr5RE/njpJB69ajLF5bWc9+e5/O3jTTS3UAn9uflbGZKdxrSRed3Q0thyzlEDSIqP69Cw\n3YqiCvr3SiHPXaI+loQ1IInI2SKyVkQ2iMhtLexPFpEX3f2fichQd/tQEakRkaXux6PhbLcx0eLs\n8QOY9aNpTBuZy6/fWs2Vf/uUwrKDc2HW7Kzk8y2lXHXCEOLirG5dqGWlJnL6mL68uayYxnaGUn1W\nFFfG5PMjCGNAEpF44GHgHGAscIWIjA047NtAmaqOAB4A7vXbt1FVJ7kf3w1Lo42JQnmZyfz1G1O4\n7+IJLC+s4Oz/+5iXFm5HVXl2/laSE+L42jE9d9HCSDPj6IHs2V/PJxv3tntsdX0jG0v2Mz6G1kDy\nF84e0nHABlXdpKr1wAvAhQHHXAg87X7+MvAlsfLDxnSYiHDpsfm886NpjB3Yi1teXsZ1zyzitSVF\nfGXiQPrEQOHZSHHq6L5kpiTwuodhu9U7KlGNvQoNPuEMSIOA7X5fF7rbWjxGVRuBCsC3QEuBiCwR\nkTkickqoG2tMT5CfncYL153A7eceyUfrSqiub+JqS2YIq5TEeM4dP4BZK3dSU9/U5rEripwMu1gd\nsouW4qo7gCGqulf+f3vnHqRnVd/xzy8JG3IhITdy2QSCTMBchARSmzqdlGoKBSQXLTP5wxpqqdMq\nI8gw1NqpooVWKBYHYWhTZaS2o6hYiAJGEFCcoZFbhGyWsLlIyGaziYFmF8x199c/fuclT9687+4m\n2X3eZ7Pfz8w7++5zOef7/M7ld855znuO2UXAQ2Y2293bsheZ2SeBTwKceaZ+XyEExPp+f7XwPVx8\n3gTWt7RxwbTTay1pwLFk3hQeeP4NHm9sZfEFU6pet655D+NH1jFx1MCb0AD59pCagezA9dR0rOI1\nZjYEGA3sdvf97r4bwN1fADYB55ZH4O4r3X2+u8+fMEEziITIMmPiaSyZWz4oIfJgwdnjmDTq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LNUlySUIckImtEZLuI7BSRL4RZf6OIbBWR10XkSRGpDlrXLyKb7Mc/E2m3ooyGpg4/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\nNsa4UtgFs2p2KflZ6VrbLgR1SIoygSnNy8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wEdgU9LwdO6ub4L+AEv4BsESnDac77iao+5aXgxiRCdQ+Wngg4PFtDC+OzPDdiGNPn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\nLAbnZiZFP9Lqcmeql3hm2AVMHZ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PngBWyq/Dk0eFTtYZMxG1Rw0PABwFExANsw5p/9KSIvD1OY1OUtGS2k2IBStI0ZDfg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\nfaPUlydW33F3qw8RWBn2ua+xKy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pIT7y6/qYB16jq/gTKYhgGRy0qL1kpglgwhZHOeLWoVgCzB+xlGMagqWvxU5SbRUGO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\n9dM1yKUNA7HsrTqe31zLbRf3vy5sYmk+k0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glxv3KcJ1jmW8wuyIqx594eY4OId8W4DEv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\nKjV670wAAAAASUVORK5CYII=\n","text/plain":["<Figure 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aG1D0STJgUC6ZJn2F23GpIdVh17MCKtGuw3z8JvCjTkzJkn4ODE3Gd7E5ybKGahIqN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\nZ2awamn+hGwNLx/uorNv2CbDJkBVaR6q562QdBpDml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3AZ2I5EIMhU2nvH6asMJe8HK8/x8MpKcilsigvJVLUVZWugZFp6fw5lBfmjqlcGNIb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\nMAKNGSrDMAwj0JihMgzDMALN/wNxZszPXdvwOAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"fk22hJOxz3Td","colab_type":"text"},"source":["###Identifying inconsistencies\n"]},{"cell_type":"markdown","metadata":{"id":"ZA2-vC_ez5A4","colab_type":"text"},"source":["####House ads conversion rate\n","\n","In this exercise, you'll try out your `conversion_rate()` and `plotting_conv()` functions out on `marketing` looking at conversion rate by `'date_served'` and `'marketing_channel'`."]},{"cell_type":"code","metadata":{"id":"ujh3wdDL0CXJ","colab_type":"code","outputId":"2a41fa96-0085-4261-a7ef-6131b832e896","executionInfo":{"status":"ok","timestamp":1575109017773,"user_tz":-480,"elapsed":771,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["# Calculate conversion rate by date served and channel\n","daily_conv_channel = conversion_rate(marketing, ['date_served', \n","                                                 'marketing_channel'])\n","\n","print(daily_conv_channel.head())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["date_served  marketing_channel\n","2018-01-01   Email                1.000000\n","             Facebook             0.117647\n","             House Ads            0.084656\n","             Instagram            0.106667\n","             Push                 0.083333\n","Name: user_id, dtype: float64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"itjOK5vQ0HPi","colab_type":"text"},"source":["- Unstack `daily_conv_channel` with level equal to one and convert the result into a DataFrame.\n","- Try out your `plotting_conv()` function on `daily_conv_channel`."]},{"cell_type":"code","metadata":{"id":"12yswNHHz53H","colab_type":"code","colab":{}},"source":["# Calculate conversion rate by date served and channel\n","daily_conv_channel = conversion_rate(marketing, ['date_served', \n","                                                 'marketing_channel'])\n","\n","# Unstack daily_conv_channel and convert it to a DataFrame\n","daily_conv_channel = pd.DataFrame(daily_conv_channel.unstack(level = 1))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"30GkD-sn0QsA","colab_type":"code","outputId":"ba70cda5-48f7-4e43-ab1d-aa80c61b91b4","executionInfo":{"status":"ok","timestamp":1575109073191,"user_tz":-480,"elapsed":1827,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Plot results of daily_conv_channel\n","plotting_conv(daily_conv_channel)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEuCAYAAABVgc95AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHqBaq5OIb4+kzkJpg7nlkm/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m/S4S3U5qBBCy0RGRF4vIfSJyRETe1uL180XkCyLyLRH5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\nCN1EP+dXSjpdeotg7x66tFbpe42OhaVKUFjfYMKWHq0VpjVRxPHRqIw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e28tyOWq5eUcJli4tYOj2HuLj+nfKbZubyg9XbaWjrCskSACP6+FL9qerewTaf15oG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\n/lsYGjkzDwXYrqoPx6Eup8jDuThnjxf9cYm6csyf+g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YxUW0eNKZJDY3Kks1FzF2Vz7vf7KSgrIaYyAg++vnxZKTGd8o+TOD4m5Be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\n+s5hfHjLcdx88ug2JaOmxEVH8v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+Xwih0abBw8uTY1l4vackusqfo0hDMtqp87n62lsERHxkpM+9ztmeIg809/NMNq2fc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\n96Y46aOEa09dijEe564F5re3/hLTfyOxvDmEzglrE8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0kFX2cBqQrgG8aY9ZzYhLDVk5OclCq3wvWInqp7fXsonMe6a5n1/HYwp0BO9+fP9rN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\nBUEQBJkgHFIQBEGQCcIhBUEQBJng/6lFk+AvTu5SAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"o5iRd8uj0awU","colab_type":"text"},"source":["####\n","Analyzing House ads conversion rate\n","Now that you have confirmed that house ads conversion has been down since January 11, you will try to identify potential causes for the decrease.\n","\n","As a data scientist supporting a marketing team, you will run into fluctuating metrics all the time. It's vital to identify if the fluctuations are due to expected shifts in user behavior (i.e., differences across the day of the week) versus a larger problem in technical implementation or marketing strategy.\n","\n","In this exercise, we will begin by checking whether users are more likely to convert on weekends compared with weekdays and determine if that could be the cause for the changing house ads conversion rate."]},{"cell_type":"markdown","metadata":{"id":"ST9UKd1F0c84","colab_type":"text"},"source":["- Add a day of week column to the `marketing` DataFrame using `dt.dayofweek` based on the `'date_served'` column.\n","- Use `conversion_rate` to calculate conversion by the day of week and marketing channel and store the results in `DoW_conversion`.\n","- Create a line plot of the results, set the y-axis to begin at 0 and display the plot."]},{"cell_type":"code","metadata":{"id":"CGI_jdvF0qfB","colab_type":"code","colab":{}},"source":["# Add day of week column to marketing\n","marketing['DoW_served'] = marketing['date_served'].dt.dayofweek"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lmT6BizP0sxb","colab_type":"code","outputId":"9fbf4bae-c222-4b0d-ab73-9553358f1c98","executionInfo":{"status":"ok","timestamp":1575109205439,"user_tz":-480,"elapsed":737,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["marketing.tail()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>date_served</th>\n","      <th>marketing_channel</th>\n","      <th>variant</th>\n","      <th>converted</th>\n","      <th>language_displayed</th>\n","      <th>language_preferred</th>\n","      <th>age_group</th>\n","      <th>date_subscribed</th>\n","      <th>date_canceled</th>\n","      <th>subscribing_channel</th>\n","      <th>is_retained</th>\n","      <th>DoW</th>\n","      <th>DoW_served</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>10032</th>\n","      <td>a100007290</td>\n","      <td>2018-01-17</td>\n","      <td>Email</td>\n","      <td>control</td>\n","      <td>True</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>45-55 years</td>\n","      <td>2018-01-17</td>\n","      <td>2018-01-24</td>\n","      <td>Email</td>\n","      <td>False</td>\n","      <td>2.0</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>10033</th>\n","      <td>a100007291</td>\n","      <td>2018-01-17</td>\n","      <td>Email</td>\n","      <td>control</td>\n","      <td>True</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>55+ years</td>\n","      <td>2018-01-17</td>\n","      <td>NaT</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>2.0</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>10034</th>\n","      <td>a100007292</td>\n","      <td>2018-01-05</td>\n","      <td>Instagram</td>\n","      <td>control</td>\n","      <td>False</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>55+ years</td>\n","      <td>2018-01-17</td>\n","      <td>NaT</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>10035</th>\n","      <td>a100007293</td>\n","      <td>2018-01-17</td>\n","      <td>Email</td>\n","      <td>control</td>\n","      <td>True</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-17</td>\n","      <td>NaT</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>2.0</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>10036</th>\n","      <td>a100007294</td>\n","      <td>2018-01-06</td>\n","      <td>Facebook</td>\n","      <td>control</td>\n","      <td>False</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-17</td>\n","      <td>NaT</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>2.0</td>\n","      <td>5.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          user_id date_served marketing_channel  ... is_retained  DoW DoW_served\n","10032  a100007290  2018-01-17             Email  ...       False  2.0        2.0\n","10033  a100007291  2018-01-17             Email  ...        True  2.0        2.0\n","10034  a100007292  2018-01-05         Instagram  ...        True  2.0        4.0\n","10035  a100007293  2018-01-17             Email  ...        True  2.0        2.0\n","10036  a100007294  2018-01-06          Facebook  ...        True  2.0        5.0\n","\n","[5 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":53}]},{"cell_type":"code","metadata":{"id":"sgqNmrWB0khN","colab_type":"code","colab":{}},"source":["# Calculate conversion rate by day of week\n","DoW_conversion = conversion_rate(marketing, ['DoW_served', \n","                                             'marketing_channel'])\n","\n","# Unstack channels\n","DoW_df = pd.DataFrame(DoW_conversion.unstack(level=1))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NOmorOsS01HL","colab_type":"code","outputId":"3a5944a8-1b09-4bd8-eb5a-f9c67613b4b5","executionInfo":{"status":"ok","timestamp":1575109223845,"user_tz":-480,"elapsed":805,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":235}},"source":["DoW_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>marketing_channel</th>\n","      <th>Email</th>\n","      <th>Facebook</th>\n","      <th>House Ads</th>\n","      <th>Instagram</th>\n","      <th>Push</th>\n","    </tr>\n","    <tr>\n","      <th>DoW_served</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0.0</th>\n","      <td>0.162621</td>\n","      <td>0.119601</td>\n","      <td>0.062660</td>\n","      <td>0.122517</td>\n","      <td>0.064516</td>\n","    </tr>\n","    <tr>\n","      <th>1.0</th>\n","      <td>0.906250</td>\n","      <td>0.147887</td>\n","      <td>0.070312</td>\n","      <td>0.151943</td>\n","      <td>0.115854</td>\n","    </tr>\n","    <tr>\n","      <th>2.0</th>\n","      <td>0.837209</td>\n","      <td>0.127036</td>\n","      <td>0.075269</td>\n","      <td>0.150160</td>\n","      <td>0.105882</td>\n","    </tr>\n","    <tr>\n","      <th>3.0</th>\n","      <td>0.727273</td>\n","      <td>0.133333</td>\n","      <td>0.059034</td>\n","      <td>0.143498</td>\n","      <td>0.067797</td>\n","    </tr>\n","    <tr>\n","      <th>4.0</th>\n","      <td>0.666667</td>\n","      <td>0.110132</td>\n","      <td>0.062278</td>\n","      <td>0.129870</td>\n","      <td>0.055556</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["marketing_channel     Email  Facebook  House Ads  Instagram      Push\n","DoW_served                                                           \n","0.0                0.162621  0.119601   0.062660   0.122517  0.064516\n","1.0                0.906250  0.147887   0.070312   0.151943  0.115854\n","2.0                0.837209  0.127036   0.075269   0.150160  0.105882\n","3.0                0.727273  0.133333   0.059034   0.143498  0.067797\n","4.0                0.666667  0.110132   0.062278   0.129870  0.055556"]},"metadata":{"tags":[]},"execution_count":55}]},{"cell_type":"code","metadata":{"id":"_OT88NvG0nSW","colab_type":"code","outputId":"ed3d92af-6049-40ad-fc1d-c0c1d7221414","executionInfo":{"status":"ok","timestamp":1575109249671,"user_tz":-480,"elapsed":852,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":311}},"source":["# Plot conversion rate by day of week\n","DoW_df.plot()\n","plt.title('Conversion rate by day of week\\n')\n","plt.ylim(0)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAEmCAYAAACZEtCsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niK5I/jPPwqaCo5SfaOg2XTKnKyLYwvyRfZk/si9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t70WCu9PXuYdpsDnkwSXhc8wmxYi+\nEYzoG8Gt553ZKb7ofqSTzSkzq4S4sADGDojydlGEEOKsSXAHTtTbWLHrMPPSestTeEJ40MqVK5k/\nf36H02/bto2lS5e65pcsWcKf/vSnzihaE2+88QZ33313p++nNYmJiRw5csSjeUpwB1buKqPe5pC7\nZITwIJvNdtrbNA/uCxYs4IEHHvBksXoMCe5AZnYxMaH+jE+M9nZRhPC6wsJCUlNTWbhwIUOGDOH6\n669n+fLlTJ06lZSUFDZt2sSmTZuYPHkyY8aMYcqUKezatQswWsALFixg1qxZzJ49u0m+mzdvZsyY\nMeTl5XHixAluueUWJkyYwJgxY/j4449paGjgkUce4d1332X06NG8++67TVrUCxcu5N5772XKlCkk\nJyfz/vvvA+BwOLjrrrtITU1lzpw5XHzxxa51Ldm8eTNTpkxh1KhRTJgwgaqqKgAOHTrE3LlzSUlJ\n4Ve/+pUr/Z133klGRgYjRozg0UcfdS1PTEzk0UcfZezYsaSnp5ObmwvAY489xi233MKMGTNITk7m\nhRdecG3z73//mwkTJjB69Gh+8pOfYLfbz+ZX1TattVc+48aN011BbYNND3s4Uz/44ffeLooQ51xO\nTs4pywoKCrTZbNbff/+9ttvteuzYsfrmm2/WDodDf/TRR/rSSy/VlZWV2mq1aq21/vLLL/UVV1yh\ntdb69ddf1/369dPl5eVaa61XrFihL7nkEr127Vo9duxYvW/fPq211g8++KB+6623tNZaHzt2TKek\npOjq6mr9+uuv65/97GeusrjP33TTTfqqq67Sdrtd79ixQw8aNEhrrfV7772n582bp+12uy4uLtaR\nkZH6vffea/H71tfX66SkJL1p0yattXZ9j9dff10nJSXpiooKXVtbqwcMGKCLioq01tr1XWw2mz7/\n/PP19u3btdZaDxw4UL/wwgtaa61ffPFFfeutt2qttX700Uf15MmTdV1dnS4rK9PR0dG6oaFB5+Tk\n6Pnz5+uGhgattdZ33nmnfvPNN115lZWVdej3A2zRHYixPf5umVW7y6hpsHOxjCUjhEtSUhLp6ekA\njBgxgtmzZ6OUIj09ncLCQiorK7npppvYs2cPSimsVqtr2zlz5hAdffIseOfOndxxxx0sW7aMvn37\nArBs2TKWLFnCM888A0BdXR1FRUXtluuyyy7DZDIxfPhwSktLAfjmm2+4+uqrMZlM9O7dm5kzZ7a6\n/a5du+jTpw/jx48HIDz85DAjs2fPJiLCeMZl+PDh7Nu3j/79+/Pf//6Xl19+GZvNRnFxMTk5OYwc\nORKAK664AoBx48bx4YcfuvK65JJLCAgIICAggLi4OEpLS/nqq6/YunWra9+1tbXExcW1+53PVI8P\n7plZxUQFW5iYLF0yQjQKCDg5EqTJZHLNm0wmbDYbDz/8MDNnzmTx4sUUFhYyY8YMV/qQkJAmefXp\n04e6ujq+++47V3DXWvPBB2zEBZkAACAASURBVB8wdOjQJmk3btzY4XJpDw+d4p632WzGZrNRUFDA\nM888w+bNm4mKimLhwoXU1dWdsk1j+rby0lpz00038cc//tGj5W5Nj+5zr7fZWb7zMBcO741FxtQQ\nosMqKyvp168fYPSztyUyMpLPPvuMBx98kJUrVwJw0UUX8be//c0VoL/77jsAwsLCXH3gHTV16lQ+\n+OADHA4HpaWlrn20ZOjQoRQXF7N582YAqqqq2rzwe/z4cUJCQoiIiKC0tJTMzMzTKpu72bNn8/77\n73P48GEAjh49yr59+844v/b06Ij2zZ4jVNfb5CXYQpymX/3qVzz44IOMGTOmQ3fFxMfH8+mnn/Kz\nn/2MjRs38vDDD2O1Whk5ciQjRozg4YcfBmDmzJnk5OS4Lqh2xJVXXklCQgLDhw/nhhtuYOzYsa7u\nleb8/f159913ueeeexg1ahRz5sxp0hJvbtSoUYwZM4bU1FSuu+46pk6d2qEytWT48OE88cQTXHjh\nhYwcOZI5c+ZQXFx8xvm1p0ePCvmL97bzxY4Stj40B3+/Hl3PiR5q586dDBvW/UdAra6uJjQ0lPLy\nciZMmMDatWvp3bv7N9pa+v14dFRIpdRc4K+AGfin1vqUpwqUUj8EHgM0sF1rfV1H8vaWBpuDZTtK\nmDM8XgK7EN3c/PnzqaiooKGhgYcfftgnAvvZaje4K6XMwIvAHOAAsFkptURrneOWJgV4EJiqtT6m\nlOq8S8Aesj6/nON1NrlLRggf0FI/++WXX05BQUGTZU899RQXXXTROSqVd3Wk5T4B2Ku1zgdQSr0D\nXArkuKW5HXhRa30MQGt92NMF9bTMrGJCA/w4L0VeJSaEL1q8eLG3i+BVHemP6Afsd5s/4Fzmbggw\nRCm1Vim1wdmNcwql1B1KqS1KqS1nM07x2bLZHXyxo4TZw+IItMjrxIQQvsdTnc1+QAowA7gWeEUp\nFdk8kdb6Za11htY6IzY21kO7Pn2bCo5yrMYqL8EWQvisjgT3g0B/t/kE5zJ3B4AlWmur1roA2I0R\n7LukpdnFBFnMnD/EexWMEEJ0po4E981AilIqSSnlD/wIWNIszUcYrXaUUjEY3TT5Hiynx9gdms+z\nS5mVGkeQv3TJCNGVmc1mRo8e7fp4avjfKVOmAMYgaWlpaR7Js6tp94Kq1tqmlLob+ALjVsjXtNY7\nlFKPYwxgs8S57kKlVA5gB36ptS7vzIKfqS2FRzlSXS8PLgnRDQQFBbFt2zaP57tu3TqP59nVdKjP\nXWu9VGs9RGs9SGv9pHPZI87AjnOwsvu11sO11ula63c6s9BnIzO7hAA/EzOHdvm7NYUQrUhMTOTB\nBx9k9OjRZGRk8O2333LRRRcxaNAgXnrpJcB4sGn27NmuIXk//vhj1/ahoaHeKvo506MGDnM4NJnZ\nxcwYGktIQI/66kKcld99soOcQ8c9mufwvuE8+oMRbaapra1l9OjRrvkHH3yQa665BoABAwawbds2\nfv7zn7Nw4ULWrl1LXV0daWlp/PSnPyUwMJDFixcTHh7OkSNHmDRpEgsWLOgxLwTvURHuu/0VlB6v\nl7tkhOgm2uqWWbBgAQDp6elUV1cTFhZGWFgYAQEBVFRUEBISwm9+8xtWr16NyWTi4MGDlJaW9pin\nV3tUcM/MKsbfbGLWMOmSEeJ0tNfC9gb3YYibD1Fss9l4++23KSsrY+vWrVgsFhITE9scJMzX9JhB\nVbTWZGaXMC0lhvBAi7eLI4ToZJWVlcTFxWGxWFixYkWnDq/bFfWYlvv3Byo5WFHLz+cM8XZRhBAd\n1LzPfe7cuR2+HfL666/nBz/4Aenp6WRkZJCamtpZxeySekxwX5pdjJ9JMWdYvLeLIoTooNZeIF1Y\nWOiaXrhwIQsXLmxx3fr161vcvrq6GjDuusnOzj7rcnZFPaJbRmtNZlYJUwfHEBEsXTJCCN/XI4J7\nTvFxio7WMC+tZ1wlF0KIHhHcM7NKMJsUF46Q4C6E6Bl8PrhrrVmaVcyk5GiiQ/y9XRwhhDgnfD64\n7y6tJv/ICXlwSQjRo/h8cF+aVYxScJF0yQghehCfD+6fZ5cwPjGa2LCA9hMLIbqU5kP+ut/meLYW\nLlzI+++/f9b5vPHGG9x9990eKJFn+fR97nsPV7OrtIrHfjDc20URQpyBzhrytyfw6Zb759nFAMyV\n/nYhfEZhYSHTpk1j7NixjB07tsnY7E899RTp6emMGjWKBx54AIC8vDzmzp3LuHHjmDZtGrm5ua70\ny5cvJyMjgyFDhvDpp58CUFdXx80330x6ejpjxoxhxYoVbS5399lnnzF58mSOHDnSmYegQ3y65b40\nq4RxA6PoHRHo7aII0b1lPgAlWZ7Ns3c6zGt7KAH34QeSkpJYvHgxcXFxfPnllwQGBrJnzx6uvfZa\ntmzZQmZmJh9//DEbN24kODiYo0ePAnDHHXfw0ksvkZKSwsaNG7nrrrv4+uuvAaOi2LRpE3l5ecyc\nOZO9e/fy4osvopQiKyuL3NxcLrzwQnbv3t3q8kaLFy/m2WefZenSpURFRXn2WJ0Bnw3uhUdOkFN8\nnIcuGebtogghzlBL3TJWq5W7776bbdu2YTabXQF2+fLl3HzzzQQHBwMQHR1NdXU169at4+qrr3Zt\nX19f75r+4Q9/iMlkIiUlheTkZHJzc/nmm2+45557AEhNTWXgwIHs3r271eUAX3/9NVu2bGHZsmWE\nh4d33gE5DT4b3DOzSwCYly5dMkKctXZa2OfSc889R3x8PNu3b8fhcBAY2PqZucPhIDIystV+++Yv\n7jjTF3kMGjSI/Px8du/eTUZGxhnl4Wk+2+f+eXYxoxIi6BcZ5O2iCCE8qLKykj59+mAymXjrrbdc\ng4vNmTOH119/nZqaGgCOHj1KeHg4SUlJvPfee4DxUOP27dtdeb333ns4HA7y8vLIz89n6NChTJs2\njbfffhuA3bt3U1RU1OZygIEDB/LBBx/w4x//mB07dpyzY9EWnwzuB47VsP1ApbTahfBBd911F2++\n+SajRo0iNzeXkJAQwBgOeMGCBWRkZDB69GieeeYZAN5++21effVVRo0axYgRI5q8S3XAgAFMmDCB\nefPm8dJLLxEYGMhdd92Fw+EgPT2da665hjfeeIOAgIBWlzdKTU3l7bff5uqrryYvL+/cHpQWKK21\nV3ackZGht2zZ0il5/3NNPk98tpNVv5zBwF4hnbIPIXzBzp07GTZMrkt1VS39fpRSW7XW7fb9+GTL\nfWlWMSP6hktgF0L0WD4X3Isra/m2qIKLpUtGCNGD+Vxw/8J5l8xcGbtdCNGD+VxwX5pdwtD4MAbF\nhnq7KEII4TU+FdwPV9WxufAo89Kl1S6E6Nl8Krh/saMUrZH+diFEj+dTwT0zq5hBsSGkxEmXjBC+\nIDS06f+yN4fXtdlsxMbGugYka8nKlSuZP3/+OSxV63wmuJdX17Mhv5yL0/uc8SPEQgjRmi+//JIh\nQ4bw3nvv4a3ng06HzwT3L3NKcWi5S0aInqKwsJBZs2YxcuRIZs+eTVFREXDqSzgaW//FxcVMnz6d\n0aNHk5aWxpo1awBYtmwZkydPZuzYsVx99dVUV1e3uL9FixZx3333MWDAANavX+9a/vnnn5OamsrY\nsWP58MMPXctXrVrlesnImDFjqKqq8vgxaIvPDBy2NLuEgb2CGd6na4zIJoQveWrTU+QezW0/4WlI\njU7l1xN+3WYa9yF/wRgvZsGCBQDcc8893HTTTdx000289tpr3HvvvXz00Uet5vWf//yHiy66iN/+\n9rfY7XZqamo4cuQITzzxBMuXLyckJISnnnqKZ599lkceeaTJtnV1dSxfvpx//OMfVFRUsGjRIqZM\nmUJdXR233347X3/9NYMHD+aaa65xbfPMM8/w4osvMnXqVKqrq9sc4Kwz+ETLvaKmgXV7jzAvTbpk\nhPAljUP+Nn4ef/xx17r169dz3XXXAXDjjTfyzTfftJnX+PHjef3113nsscfIysoiLCyMDRs2kJOT\nw9SpUxk9ejRvvvkm+/btO2XbTz/9lJkzZxIUFMSVV17JRx99hN1uJzc3l6SkJFJSUlBKccMNN7i2\nmTp1Kvfffz8vvPACFRUV+Pmd27a0T7Tcv8wpxebQXCy3QArRKdprYXclfn5+OBwOwBjyt6GhAYDp\n06ezevVqPvvsMxYuXMj9999PVFQUc+bMYdGiRW3muWjRIr755hsSExMBKC8v5+uvvyY2NrbVbR54\n4AEuueQSli5dytSpU/niiy9ITU31zJfsAJ9ouWdml9AvMoj0fhHeLooQ4hyZMmUK77zzDmCM/Dht\n2jQAEhMT2bp1KwBLlizBarUCsG/fPuLj47n99tu57bbb+Pbbb5k0aRJr165l7969AJw4caLJ25UA\njh8/zpo1aygqKqKwsJDCwkJefPFFFi1aRGpqKoWFha5RIN0riby8PNLT0/n1r3/N+PHjm7ze71zo\n9sH9eJ2VNXvKuDi9t3TJCNGD/O1vf+P1119n5MiRvPXWW/z1r38F4Pbbb2fVqlWMGjWK9evXu4YE\nXrlyJaNGjWLMmDG8++673HfffcTGxvLGG29w7bXXMnLkSCZPnnxKEF68eDGzZs1qMrzvpZdeyief\nfIJSipdffplLLrmEsWPHEhcX50rz/PPPk5aWxsiRI7FYLMybN+8cHJWTuv2Qvx99d5D/9+42Prhz\nCuMGev+9hUJ0JzLkb9fWo4f8XZpVTO/wQMb0j/R2UYQQosvoUHBXSs1VSu1SSu1VSrX6eJZS6kql\nlFZKnZOXCFbX21i5u4y5ab0xmaRLRgghGrUb3JVSZuBFYB4wHLhWKTW8hXRhwH3ARk8XsjUrcg/T\nYHPIWDJCCNFMR1ruE4C9Wut8rXUD8A5waQvpfg88BdR5sHxtyswuJjYsQPrahRCimY4E937Afrf5\nA85lLkqpsUB/rfVnbWWklLpDKbVFKbWlrKzstAvrrrbBzorcMi4aEY9ZumSEEKKJs76gqpQyAc8C\n/9NeWq31y1rrDK11Rls3/3fEqt2HqbXauThNumSEEKK5jgT3g0B/t/kE57JGYUAasFIpVQhMApZ0\n9kXVpVklRIf4MyEpujN3I4TwouZD/nbURx99RE5OjodL0710JLhvBlKUUklKKX/gR8CSxpVa60qt\ndYzWOlFrnQhsABZorc/+JvZW1FntfLWzlItGxONn7vZ3cwohPMyTwd1ms3kkn3Ot3ciotbYBdwNf\nADuB/2qtdyilHldKLejsArZkzZ4jnGiwM0+6ZIToEVauXMmMGTO46qqrSE1N5frrr3eNqf7AAw8w\nfPhwRo4cyS9+8QvWrVvHkiVL+OUvf8no0aPJy8vjlVdeYfz48YwaNYorr7ySmpoawBgiYNKkSaSn\np/PQQw+5zhRWrlzJtGnTWLBgAcOHGzcHXnbZZYwbN44RI0bw8ssvu8oWGhrKL3/5S0aMGMEFF1zA\npk2bmDFjBsnJySxZsgRv6dDAYVrrpcDSZsseaSXtjLMvVtsys4qJCLIweVCvzt6VEAIo+cMfqN/p\n2bFRAoal0vs3v+lw+u+++44dO3bQt29fpk6dytq1axk2bBiLFy8mNzcXpRQVFRVERkayYMEC5s+f\nz1VXXQVAZGQkt99+OwAPPfQQr776Kvfccw/33Xcf9913H9deey0vvfRSk/19++23ZGdnk5SUBMBr\nr71GdHQ0tbW1jB8/niuvvJJevXpx4sQJZs2axdNPP83ll1/OQw89xJdffklOTg433XSTa4jic63b\n9Wk02Bx8ubOUOcPjsUiXjBA9xoQJE0hISMBkMjF69GgKCwuJiIggMDCQW2+9lQ8//JDg4OAWt83O\nzmbatGmkp6fz9ttvs2PHDsAYNvjqq68GcA0f7L6/xsAO8MILLzBq1CgmTZrE/v372bNnDwD+/v7M\nnTsXgPT0dM4//3wsFgvp6ekUFhZ6+jB0WLcb8ndt3hGq6mwyvK8Q59DptLA7i/vAXWazGZvNhp+f\nH5s2beKrr77i/fff5+9//ztff/31KdsuXLiQjz76iFGjRvHGG2+wcuXKdvfXOOAYGN00y5cvZ/36\n9QQHBzNjxgzq6oxHeiwWi2vQQpPJ5CqnyWTyan99t2v6ZmYVExbgx9TBMd4uihDCy6qrq6msrOTi\niy/mueeeY/v27QCEhYU1ea1dVVUVffr0wWq18vbbb7uWT5o0iQ8++ADANXxwSyorK4mKiiI4OJjc\n3Fw2bNjQSd/Ic7pVcLfaHSzLKeWC4fEE+Jm9XRwhhJdVVVUxf/58Ro4cyXnnncezzz4LwI9+9COe\nfvppxowZQ15eHr///e+ZOHEiU6dObfLCjOeff55nn32WkSNHsnfvXiIiWn4nxNy5c7HZbAwbNowH\nHniASZMmnZPvdza61ZC/a/aUceOrm3j5xnFcOEK6ZYQ4Wz19yN+amhqCgoJQSvHOO++waNEiPv74\nY28Xy+VshvztVn3umdklBPubmT7k7J5uFUIIgK1bt3L33XejtSYyMpLXXnvN20XymG4T3O0OzRfZ\nJcxKjSPQIl0yQoizN23aNFc/va/pNn3umwqOUn6iQYb3FUKIDug2wT0zu5hAi4kZQ6VLRggh2tMt\ngrvDocnMLmHm0DiC/btNT5IQQnhNtwjuW4uOUVZVzzzpkhFCiA7pFsE9M6sEfz8Ts1LjvF0UIcQ5\nZDabGT16NGlpaVx99dWuAb9Ox2OPPcYzzzzTCaXr2rp8cDe6ZIqZnhJLaIB0yQjRkwQFBbFt2zay\ns7Px9/c/ZXAv0bouH9y3H6iguLJOxpIRooebNm0ae/fupbCwkLS0NNfyZ555hsceewwwBvdqHP73\nRz/6kStNTk6OaxjeF1544VwX3Su6fFM4M7sEi1kxe1i8t4siRI+15r+7ObK/2qN5xvQPZdoPh3Qo\nrc1mIzMz0zX6Ymv+9Kc/UVBQQEBAABUVFa7lubm5rFixgqqqKoYOHcqdd96JxWI5q/J3dV265a61\nZmlWMecNjiEiyLd/EUKIU9XW1jJ69GgyMjIYMGAAt956a5vpR44cyfXXX8+///1v/PxOtl0vueQS\nAgICiImJIS4ujtLS0s4uutd16ZZ79sHjHDhWy72zUrxdFCF6tI62sD2tsc/dnZ+fHw6HwzXfOPQu\nwGeffcbq1av55JNPePLJJ8nKygJaHi7Y13XplntmdjFmk2LOcOmSEUIY4uPjOXz4MOXl5dTX1/Pp\np58C4HA42L9/PzNnzuSpp56isrKS6mrPdiV1J1225d7YJTNlUC+iQvy9XRwhRBdhsVh45JFHmDBh\nAv369XMN4Wu327nhhhuorKxEa829995LZGSkl0vrPV12yN+dxceZ99c1/OHydK6bOOAclkyInqOn\nD/nb1Z3NkL9dtlsmM6sYk4ILR0iXjBBCnK4uG9yXZpcwMakXMaEB7ScWQgjRRJcM7ntKq9h7uJp5\n8uCSEJ3OW12zom1n+3vpksE9M7sEpeAieZWeEJ0qMDCQ8vJyCfBdjNaa8vJyAgMDzziPLnm3zNKs\nYjIGRhEffuZfTAjRvoSEBA4cOEBZWZm3iyKaCQwMJCEh4Yy373LBPb+smtySKh6ZP9zbRRHC51ks\nFpKSkrxdDNEJuly3TGZ2CQBz06RLRgghzlQXDO7FjO4fSd/IIG8XRQghuq0uFdz3H60h++BxGd5X\nCCHOUpcK7pnZxQDMS5PX6QkhxNnoUsF9aVYJ6f0i6B8d7O2iCCFEt9ZlgvvBilq27a+QB5eEEMID\nukxw/9x5l4x0yQghxNnrQsG9mNTeYSTFhHi7KEII0e11ieBeeryOLfuOcXG6tNqFEMITukRw/2JH\nCVojt0AKIYSHdIngvjSrmJS4UAbHhXm7KEII4RM6FNyVUnOVUruUUnuVUg+0sP5+pVSOUup7pdRX\nSqmBHS1AWVU9mwqOMk+6ZIQQwmPaDe5KKTPwIjAPGA5cq5RqPqrXd0CG1nok8D7w544WYFlOCQ4N\n82QsGSGE8JiOtNwnAHu11vla6wbgHeBS9wRa6xVa6xrn7Aagw+NUfp5dQlJMCKm9pUtGCCE8pSPB\nvR+w323+gHNZa24FMltaoZS6Qym1RSm1paysjGMnGliXV868tN4opTpeaiGEEG3y6AVVpdQNQAbw\ndEvrtdYva60ztNYZsbGxfJlTit2h5RZIIYTwsI68rOMg0N9tPsG5rAml1AXAb4Hztdb1Hdn50uxi\n+kcHMaJveEeSCyGE6KCOtNw3AylKqSSllD/wI2CJewKl1BjgH8ACrfXhjuzY7tCs3XuEi9P6SJeM\nEEJ4WLvBXWttA+4GvgB2Av/VWu9QSj2ulFrgTPY0EAq8p5TappRa0kp2LsfrrFjtWt64JIQQnaBD\n71DVWi8FljZb9ojb9AWnu+PjtTZSIgIZ3T/ydDcVQgjRDq89oVpVZ2WudMkIIUSn8Fpw18hYMkII\n0Vm8Ftz9TIqxA6K8tXshhPBpXgvu/SKDMJmkS0YIITpDhy6odobwIIu3du1RjtparMXFWA8ewnrI\n+Sk+hK6tA6XAZEKZFCjTKdOYlHHNQTmnTaZT0ynVbF1L0y3l4TZ9Gnkok1HmU/PAmHbOK4sF5W9B\n+ftj8vdHtfSxWIxthBDnnNeCe3egtcZRWXkyaB861DSIHzqE/dixphuZFJYwP0wBZjRmMJkBIyBq\nTMY0Cq0VaA1ao7UDHBocDnA40LrZtHPefTlae+GInAGLBZPF0nLw9/dH+VuMysHS+vomFcgp6dqr\nYNz24e8Pfn5yEV/0CD06uGuHA1vZEayHDjYN4IcOYTt4COuhgzhqaptsoywmLGEmLMFWAqNrsCTY\nsYQ4P8E2/HpFo6JijOB74gicKAOHteUCBEZCSKzzE9Ns2n0+1kjr1gp2D/o4HGg4Oe3QoBunHa5K\n5NR1zdPRyjrntCsPB7rBirY2oBtOfhyuaWuT5bqhAW1tfb2jphbdUOmW5tTtPVaZKdV+BRMQgCk0\nFHNoCKaQUExhYZhCQzCHhmIKdZsOC8MUGoopxJhXFt84GxW+waeDu25owFpS0mKL23rwANaSUrDZ\nmmxjDjThF6qxBNUTnNDgDNrO4B1uwRzXFxXZHyISIKI/RPQ7OR3eFyxBzQqhoa7yZKBv/NSUu80f\ngSO7Yd9aqDmKEWWbUWa3gB+DalYpqOCYppWEf6jR1eIDtNZgs7lVIKdWLO1WMNYOVECNlVBdPdYD\nB6ivrsZRXY29uhrs9nbLqQIDnZVCqBH0Q0Mxh4UaFURoKKYw57oQt2lnhWEODXFto8zmc3BUha/r\n1sHdceLEKS1uq7PFbT14ANuRo6e0+PxCFJZgO0FBdYSnuAXuUI1fXAzmmL7OYN1C8A6KOv2AqRQE\nRRqfmMHtp7fboPZo08DvXimccFYKx7YY6xqqWs7HL6iVM4LG6V4np4NjwM//9L7XOaSUAosFZbFg\nCunAC9QdDrA3nPzY6ptNW8HuXGZraDbdANpuHJPQeAiNQ4fGo+3KCPRV1ThOOIN+VRWO6hPOCuDk\ntKO6Cnt1NY7qEzTsK2qyDoej/e8bHIw5JMR1ZmAEfudZQmgIZvdp19mDsyJprDBCQuR6Rw/XZYO7\n1hr7sWOntrgPHcR6YD/WQ4dwVJ1oupEJLKFgCaonJNSOJc6OJcRmBO+oYCx9+6GiEloO3mF9wNwF\nTqvNfhAaZ3w6wlrrVgE0rwic81XFUJLVThdRRCuVQCwE92o631jJ2a0tB9BTAmoLAdRe7zbd0W06\nGKgdtpa/4xlSgAoIxxQaj58z4BPW2/gZFw+hfY2KIKw3BEU36T5zp7VG19Rgrz5hVBBVJysBR3WV\n6yzB4axAXOuqqrCVlRnbVVXhOHGiQ91UppCQk2cMrrOHMMxRkQQkDyIgZTABKSn49erl0eMlOo/u\nQOOgkVeDu7W4+NQLlQcPYD1QhLXkMLq+oUl6kwUsIXb8gqwExduxJBv93JZQhaVPHH59+htdJuFu\nre2IBCOAB/joy0AsQRDZ3/i0R2uoP956JeDqItoD+9a13kWEamX52VDgFwDmAKOS9XP+NAcYZxVm\nf2PaPxjMkca8X4Bzuf+p837O9E2mLc3SNJ927k8po9usqhSqS6G6BKoPG9NVpVC8HfYsg4bqFr6G\n2Vk5xzsDfrxrWjk/prB4iO4DluQzOlLa4cBRU+uqEE49o6g+ubzJGUU11pISbEeO4KisdOVnjooi\nYPBgV7APGDwY/8GD8YuS51C8xdHQgHXfPurz8mkoyDd+5udTX1jY4TyU9tJdF2lBQfq9gYlNlpkD\nNZYgq9sFSufPXmFY+vbFFN8fFTng1OAdEttqa0mcBbsNao+dWhHUHAGUW+BsJ1i6T7cYbAOMu4q6\n2zWC+mpn8HcG/sZPVWnT+RNlxgXp5gLCT1YC7mcDob1PVhDtnA2cCa01trIyGvbupX7vXur37KV+\nzx7q9+41uo6czLExRtAfnOIM/ikEpAzGHOajDSUvsB8/bgTtZkG84cCBJtd5/Pr2ISB5EP7JSfT5\n7W+3aq0z2svba8F9dJS/XnFJLyzhflh6x2Hpl4ApdoDzwmS/k10n4f3AEuiVMp4urTXHj9RyuLAK\na4Mdi78ZP38TfgFm57Qxbwkwpi3+JkxmqZR8nsNuVIquiqCkWSVw+OTZQUtnAyY/CIlzC/jxbpVC\nszOE5hf0T4PWGltpqRHo9zQG/j3U5+Wha2pc6fzi45sE+4DBg/EfNBhzaAeuh/RAWmtsJSVG4M7b\nS/3ePTQUFFBfUIi9/KgrnbL44d83Hv/+8fj3iyWgby/8+0QSEBeOyaJd3Y5q6r1dO7hnjBqht6xf\nc2YXKbuImuMNHN53nNLC4xwuNH7Wnzi9/l6TWRnB3mJUAkbQP7US8PM3OysJ57S/2bne5Ko4Gufd\n8zBbTHJfd3fiOhtwC/xVJU0rherDHTsbCHM7K3A/GwiONh5MQ7n97zVOq6b/j0qhHRprSSn1eQXO\nAJVvTOcXoOtPvpfHBHFc4AAAEx1JREFU0rcP/oMGOVv7zm6e5GRMwcFGvs78ztn/u9bG9ZfG6zS2\nerdrPc6ftrpTl9nrm23TyrbN0umGehqO1FBfVkdDeT315TYaKhzUV4C2nfzOJouDgHAb/uE2AsKt\nzp/GtUHVgbae+t3xLh7cMzL0li1bvLLvM2Gtt1NWVNUkkFeV1wHG32p03xDiEsOJTwwnbmA4ASF+\n2Boc2Brs2BrsWOvdpp3LrfV2bA0OrM7ltvqT69y3MdY7sNXbcThO8/elaFJBNFYkjRWHa12ziuSU\nisKtIrEEmAmJDMDsJ2cdXmO3GdcFmlwPKGl6FlDlrAysNe3ndwa0A6wnzNRXWqiv9DM+xy00HPdD\nOxqDmcYSYicg0kpAuI2ACBsBEUZAM5ndAn6LlcvpLHNb53CcDLyeujakTK7rP3Z7AA1V/tQfN9NQ\naaL+mKbhmIOGCluT3flFBBAQH4p/fBgBvSPx7xtNQL9YzFHhKL/Ak92Tjd2ZTX4GuK1vmk4FRXQo\nuHfZu2W8yWF3cLT4BKUFjYG8iqOHql03KIRFBxKXGE76+QnEJ4UR0z8M/8BzcyjtdiPIN6kUGhzO\niuJkRdA470rXbBtrvZ2a4w2nVB42a8euxiuTIiI2iMj4YKJ6N35CiIwPJjCkC9x15OvMfkbLPCy+\n/bTuZwNVJVBX4bzbRje96+aUZbrNZUpr/AF/NGFu6bTdQcPhSuoPVVB/6Bj1B49RX1xB9a5K40ls\nAKXwjw0loG8kAX0iCOgTjn/vcALiQlF+5mb71W7lo4VlzdIpU4eC5MmfpwZabfbHVl5JfdFBGvYd\noL6gkIY9BdTn52EvOwI4jI/FQkDiQALGJxM2KJmA5GT8k5MJSEzs2G27najHB3et9f9v71xjJLmq\nO/479eyeR+97Zzder/fBEmdDgjHIJMZAEhQCCQpEgiQWTgKJxBeTYOWlECUi5EuCIkE+EAiIh8wr\nJoJYQnkSBSLI8vIb4xdeL17WxjuzXu9jXt3VVXXy4d7p6Z7dmend7d2eKZ+fVLq3bt2qPtUz/b+3\nzr33FNMnm0w+ueheOXF0uiNy6UjExJ4Ge1+8x/XK9zQYaQxvTngYBoQjAenI5bm+lkrePv+TRWe/\nlXPmxDynj89xanKOHz58kjJfFIn6eOyEfscIm326aWKE8c01F7vGuLKkY27bsv+KfJwAqd+60Swj\nO3q0M3i7MJA7/d2HF+f/RxHJnmvcIO6BF3TSZPduJBq8XGmWkR07RuuxJ8iOOPHOjvyA7MgRyq5x\nhmB8nHTfPsZueiXpfi/g+/YR79p1WewaBM87t8z8TMbUk73uleaMm/sdRgHbdo8tulf2NNiwrW4+\n61Uoi5KzJ5tO7I/PcWpyltPH53ju+GzPGEQYB4s9/YmRTgOwcWKEOLFVmc9XylbLDTB2zdppHT5M\n+9ixTm9d4tgJ6pIpm/GuXX2t6C2mp92slCM/IDvyhEufeILs2LHeWSk7d5Lu3evGDvbtJdnn0nDr\n1jWjAyJiPvd2VvDsEj/52WednxyBzTtHOyI+safB5qtGCW32ykCZn8k4dXzOC/8spyZdAzD97HyP\nR2B8c41NO0ZcL3/HKJsmXH6kkayZH5VxZSnn52k9ccTNLjl8mObjj5M9fpj2j37UqSNpSrJ/H7UD\nB0j8QG6QpueIeH7ixOKF45jkmt2ke/eRdFwp+0n3Dt+V0g/PO3EvS+XUM7M97pWTT8+6oFfA2Oa0\nR8i37b5yfnLjXPJ2wZmpeSf8k7M898wcpyddA5Bni37/pB51evod4d8xQmNb3Rri5ynFzKyfUtg7\nRz+fnOypF4yNefF288PT/ftJ9u4l2bVrXQd5q7S4qyrTzzV73CtTP5wmb7nHq3Qk6nGtbL9mnNEN\nSz2AxlpES2XmdMv782edm+f4HKePzzJ7ZnHFchAIjW31zkBup9c/MUI6sn5/uMbFU5w9S+vwYTRr\nk+zbS7RtWyWf+voV93XRdW3OtjtulYV0fnrRT7716jEO3rizI+gbtpuffFCoKnmZ0y7b5JrTLtoU\nWjASjTAajw78e5ZAGN9cY3xzjasPbu45ls3nnJp0Qn/KD+aeOj7H0e+dpCwWOykjjcSL/aj37Tvh\nH99kA7pVJmw0GLn++mGbsWZYc+KeZwXPPjXD5A8WxfzMCR9TXWDTjlGuedGWTq98y1Vja3a+dakl\neZkvimNX2tm8YOa6cr3lzlsQ3WXrLSnruX4f5xW6fKjbUELGk3EaScNtaeO8+Q3phnPKLqZhSOpu\n5tLEnkbv91yUnH226cV+tjOwe/juSVpziwO6URx0evcbfW9/044RNm4fIbIBXaNiDFXcu/3kU0en\nnZ/8qZnOQp2xTSnb9zQ4eNOPOffK7nGSev8mt8s2WZHRKlqddLn8hZZ1HztfndWEcVAEEhBJRBRE\nxGHcyUdBRBzEPelCvhbV3P4K53Wfs/QaoYTM5/OcaZ3hbHaWs62zLs3O8vTM0539QTQMC/lOA3Ge\nhiEI3SycjRMj7P3prZ1yVWV+us3pLvfOqeNzTD55lsfvmVpccCKLA7qbJkbZsL1OOhqRpBFxLSSp\nRcRp2MlHia36vdxoqWStgmw+J5vPafm0d79YzDcXjxW5EoTitsClEghBGPSWLxwL/bElZWH3eb58\n4VqLx3qPS1e9YMlnStf1VztPFl7BeQkMTdxPHp/mI7d9ldK7USVV4omc5KUtdNs8+dZpztTmOFFk\n3Fu0yE5mtKYuTIQvVVwjiUjChDRMz0nTMKUW1diQbugpS8KEJEhIwuQcUTzvvsTLivL5zluaBv2s\nVx4CqspcPtcj/N35K9kwNDY32LizwTXJzk7DkGcFp6fmXU9/ckH4Z/nR46d7BnTPi0CchiRpSFyL\nSGpO+OPUNQJu35envnHo5H292mJ5NKAQEapKoUXnibHUkkKLc8oUJZTw3P81iQiDS3+CKUtdFOLm\nghivJNRFT91sPidrFasuLg0CIalHJPXQpbWI8S11wihAS6UslbIoKQt1W16SZ7q4X5SUpbq6RdfW\nda4WOrQ3WqqUaKAulRINSkrWQcjfVt7igc1fY2rsKFNjP+RM7QSI/xZPuS2QoEc0l+aTMGE8Ge8t\nC84V4e76tah2rlgH59ZbEGfj4hARRuNRRuNRdrLzgs7tu2HoKr+khmGsQeNgg8ZLGozHDTa0xymb\nQpGVPvS8UmZQZkrZEsoMskxotQVtC2QBnA6gHUAWIHmItEOk6E8olZIialNEbfKwTRFl5FFGO2yR\nhy7NwiZ5kNEK52mHLVrBPK1gnixo0gxnaQVNWsE87aBFEbQ7oVwulEBDRsox6uUY9XKUejlKrRgl\nLeqkZZ2kqJPkNeK8RlLUiPKEKE+J2glhHhO2E4K8j99NUEKqSFIiiRKkENYh3ARpTRhNA6JaQFwL\nSOohsRfwtB6T1iNq9Zgk9R2jrqfQhd/shTyRN4vmyvXzjCxvk7Uz2nlOVuTkeU47d2leFOR5TllA\noIHfQsSngQYIIUEZEhB00u7jLg1JSIglISImJiYSl4bERESE2r8mDW22zLUvvlY/9+XPLdsrNnE1\nLoYLahi6j/XRMHQjCKGEhEHYcY0FQeDKxJXFxMRljbSokZR14jIlKWrEeUpcpERlQpQnxEXqhLFI\niPKYMI8J8pggjwjakU9DpOyzVy0KSYnEiiRuC2IIEghStzq/yJSyiW+sBG35Ripf/UmwDAvKqE0R\nZ+RxRh5m5FGLLGrRjppkYZMsco1PM5yjFcwxH87SDGaZC6aZC2YogsG+UGVQrNQxXKkzuFz9NEr7\nqp+GKVHQ38vb1/xsmbF4jOsnbGTbGCyDeGKYyWYIJHCi7V1f3UK+IN5XmqIoaTddGIismbt8syBr\nuXzWLGgv5FsF7WZvPpsvaJ8uKPOSkXrkXBlbnPso8fupd2908vUlx+rRJU9gWHAfLZ0gsNzAfvfA\n/3Jl3ecB1KJaf+K75Km/SmMp1jU2DE93w7AWCcOAcDRY94HZRKTjRjEuH2tzNM4wDMO4JEzcDcMw\nKoiJu2EYRgUxcTcMw6ggJu6GYRgVxMTdMAyjgpi4G4ZhVBATd8MwjArSl7iLyOtE5DEROSwif3ae\n46mIfN4f/7aI7Bm0oYZhGEb/rCruIhIC/wC8HjgI3CwiB5dU+z3glKq+APgA8L5BG2oYhmH0Tz89\n9xuAw6p6RFUz4A7gjUvqvBG43ee/ALxGqhSkwTAMY53RT3CHq4BjXftPAS9fro6q5iJyBtgCPNtd\nSUTeAbzD77ZE5HsXY/Q6YStL7r9iVPn+qnxvYPe33vnxfipd0cg9qvpR4KMAInJ3P2Er1yt2f+uX\nKt8b2P2td0Tk7n7q9eOWeRq4umt/ly87bx0RiYANwMl+DDAMwzAGTz/ifhdwQET2ikgC/CbwpSV1\nvgT8js+/GfiKDustIIZhGMbqbhnvQ38n8F9ACHxCVR8Skb8G7lbVLwEfBz4tIoeB53ANwGp89BLs\nXg/Y/a1fqnxvYPe33unr/ob2mj3DMAzj8mErVA3DMCqIibthGEYFGYq4rxbOYD0jIp8QkakqzuEX\nkatF5Ksi8rCIPCQi7xq2TYNERGoi8h0RecDf33uHbdPlQERCEblPRP512LYMGhF5UkQeFJH7+50y\nuF4QkY0i8gUReVREHhGRn12x/pX2uftwBt8HfhG3IOou4GZVffiKGnKZEJFXATPAp1T1RcO2Z5CI\nyE5gp6reKyLjwD3Amyr0txNgVFVnRCQG/g94l6p+a8imDRQR+UPgZUBDVd8wbHsGiYg8CbxMVSu3\niElEbge+rqof8zMXR1T19HL1h9Fz7yecwbpFVb+GmzFUOVT1GVW91+engUdwq5MrgTpm/G7st0rN\nOBCRXcCvAB8bti1G/4jIBuBVuJmJqGq2krDDcMT9fOEMKiMQzxd85M+XAN8eriWDxbss7gemgP9W\n1UrdH/D3wJ8C5bANuUwo8GURuceHO6kKe4ETwCe9S+1jIjK60gk2oGpcMCIyBnwRuE1Vzw7bnkGi\nqoWqXodbiX2DiFTGtSYibwCmVPWeYdtyGblJVa/HRbG91btJq0AEXA98WFVfAswCK45XDkPc+wln\nYKxRvC/6i8BnVfVfhm3P5cI/8n4VeN2wbRkgrwB+1ful7wB+QUQ+M1yTBouqPu3TKeBOnBu4CjwF\nPNX1JPkFnNgvyzDEvZ9wBsYaxA84fhx4RFXfP2x7Bo2IbBORjT5fxw36PzpcqwaHqr5bVXep6h7c\n7+4rqnrLkM0aGCIy6gf68S6L1wKVmLWmqseBYyKyEBHyNcCKExmuaFRIWD6cwZW243IhIv8E/Byw\nVUSeAt6jqh8frlUD4xXAbwEPer80wJ+r6r8P0aZBshO43c/oCoB/VtXKTResMBPAnf5VEhHwOVX9\nz+GaNFB+H/is7xQfAd6+UmULP2AYhlFBbEDVMAyjgpi4G4ZhVBATd8MwjApi4m4YhlFBTNwNwzAq\niIm7YRhGBTFxN9YsIlL40K0P+TC8fyQiK/7P+rgb1/l8JCIzInJL1/F7RGTFlX3DQETeJiIfHLYd\nRnUwcTfWMvOqep2q/iRutejrgfescs4h4EaffzEuvPSN0Fm1uB94YJBGisN+S8aawv4hjXWBjxXy\nDuCdXkxrIvJJ/2KG+0Tk533Vb7Ao7jcC/whc5/dvAO5R1eJ8nyEir/ZPCvf7ay4sZf8TEblLRL67\n8AIPEdnjXzjzKdwS978Ukb/rulanJy4it/iXgNwvIh/xK2ARkbeLyPdF5Du41b+GMTBM3I11g6oe\nwYWs2A7c6or0p4CbcWEDavT23G8Evga0vFDfiBP/5fhj4FYfFfKVwLyIvBY4gGsYrgNe2hVp8ADw\nIf9k8SHg17qu9RvAHSLyEz7/Cn/dAnirf/HJe3GifhNw8CK/FsM4LybuxnrlJuAzAKr6KHAUeKGq\nHgUSEdkBXAs8hgtW93KcuB9a4ZqHgPeLyB8AG1U1xwWfei1wH3Cvv+YBX//owluaVPUEcEREfkZE\ntvh6h3ABnl4K3OXj8bwG2Oft+V9VPeFfWvP5AXwnhtHhigcOM4yLRUT24Xq+U6tU/QbwFuAZVVUR\n+Rauh3wD8M3lTlLVvxWRfwN+GTgkIr8ECPA3qvqRJbbswcXU7uYO4NdxkSTv9J8twO2q+u4l579p\nlXswjEvCeu7GukBEtuH85x9UF+3u68Bb/bEXArtxvXRw4n4bi0L+TeC3geOqemaFz9ivqg+q6vtw\nvf1rcdFLf9e/oAQRuUpEti9ziTtxr4y8GSf0AP8DvHnhHBHZLCLX4N5g9WoR2eJj5L/lgr4Qw1gF\n67kba5m6d2XEQA58GliII/8h4MMi8qA/9jZVbfljh4AP4MVdVZ/xg5gr+dsBbvMDsyXwEPAfqtry\nfvNv+lCyM8AtuCeIHlT1lIg8AhxU1e/4sodF5C9wr34LgDbOr/8tEfkrb+Np4P6l1zOMS8FC/hqG\nYVQQc8sYhmFUEHPLGM87ROTtwLuWFB9S1VuHYY9hXA7MLWMYhlFBzC1jGIZRQUzcDcMwKoiJu2EY\nRgUxcTcMw6gg/w+keaVCIhNsTQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"j57f9Y2f0-mo","colab_type":"text"},"source":["####House ads conversion by language\n","\n","Ideally, the marketing team will consider cultural differences prior to launching a campaign, but sometimes mistakes are made, and it will be your job to identify the cause. Often data scientists are the first line of defense to determine what went wrong with a marketing campaign. It's your job to think creatively to identify the cause."]},{"cell_type":"markdown","metadata":{"id":"pItDl3A71CqP","colab_type":"text"},"source":["- Create a new DataFrame `house_ads` by isolating the rows from `marketing` where `marketing_channel` equals 'House Ads'.\n","- Run your `conversion_rate()` function on the `house_ads` DataFrame grouping by `date_served` and `language_displayed`.\n","- Use the `plotting_conv()` function on `conv_lang_df` to display your results."]},{"cell_type":"code","metadata":{"id":"5px3dz3H1GuQ","colab_type":"code","outputId":"e7160c3a-ff6d-46ed-b228-f8c76c5dde04","executionInfo":{"status":"ok","timestamp":1575109326611,"user_tz":-480,"elapsed":3170,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Isolate the rows where marketing channel is House Ads\n","house_ads = marketing[marketing['marketing_channel'] == 'House Ads']\n","\n","# Calculate conversion by date served and language displayed\n","conv_lang_channel = conversion_rate(house_ads, \n","                                    ['date_served', \n","                                     'language_displayed'])\n","\n","# Unstack conv_lang_channel\n","conv_lang_df = pd.DataFrame(conv_lang_channel.unstack(level=1))\n","\n","# Use plotting function to display results\n","plotting_conv(conv_lang_df)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZ0AAAEuCAYAAABVgc95AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVpMxnXSg2emnm0ybmdVhzCedhoesNjOrjZPOgJOO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size 432x288 with 1 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NQQ7CprI646AimeulkEhsVybLFk/jNWzvYWdFAgduSLoOxr6aZ+1fs5B/r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\njhbauiXtqvOsDZk2DqmMmS0CLsa9/tmlAn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Q+SxhRugNYD/wsLj8OuCuDuGqqo6uPmTPqWdBSuU3jyKWtPPJEL9v6h3KOLL3uvegy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\nyC3pmFk9cClwEnA48C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5rpJLvwKJ5LeO2kFQ1UgspGRpgdUEVBp41xlTXhC0kEWkUkXtF5PBgmao+paq/nkQy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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"4lE5ZYMn1SNb","colab_type":"text"},"source":["####Creating a DataFrame for house ads\n","\n","As a data scientist, it is your job to provide your marketing stakeholders with as specific feedback as possible as to what went wrong to maximize their ability to correct the problem."]},{"cell_type":"markdown","metadata":{"id":"HAh4-9HC1Wgb","colab_type":"text"},"source":["- Use `np.where()` to create a new column in `house_ads` called `'is_correct_lang'` whose values are `'Yes'` if `'language_displayed'` is equal to `'language_preferred'` and `'No'` otherwise.\n","- Group by `date_served` and `is_correct_lang` to get a daily count of the ads served."]},{"cell_type":"code","metadata":{"id":"KLMKGdfg1f8z","colab_type":"code","outputId":"e4094fa2-2e92-4963-ed86-74e372b1a57e","executionInfo":{"status":"ok","timestamp":1575109418782,"user_tz":-480,"elapsed":805,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":139}},"source":["# Add the new column is_correct_lang\n","house_ads['is_correct_lang'] = np.where(\n","    house_ads['language_preferred'] == house_ads['language_displayed'], \n","    'Yes', \n","    'No')\n","\n","# Groupby date_served and is_correct_lang\n","language_check = house_ads.groupby(['date_served','is_correct_lang'])['is_correct_lang'].count()\n","\n","# Unstack language_check and fill missing values with 0's\n","language_check_df = pd.DataFrame(language_check.unstack(level=1)).fillna(0)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  after removing the cwd from sys.path.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"h8eb5OUJ1ibN","colab_type":"code","outputId":"db554ef7-3a2b-4447-9dca-db4952be41c4","executionInfo":{"status":"ok","timestamp":1575109422641,"user_tz":-480,"elapsed":747,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["language_check.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["date_served  is_correct_lang\n","2018-01-01   No                   2\n","             Yes                189\n","2018-01-02   No                   3\n","             Yes                247\n","2018-01-03   Yes                220\n","Name: is_correct_lang, dtype: int64"]},"metadata":{"tags":[]},"execution_count":59}]},{"cell_type":"code","metadata":{"id":"rpv1EeN31k38","colab_type":"code","outputId":"3df00c5a-d20c-4798-88ae-3e99cded2a19","executionInfo":{"status":"ok","timestamp":1575109424848,"user_tz":-480,"elapsed":731,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":235}},"source":["language_check_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>is_correct_lang</th>\n","      <th>No</th>\n","      <th>Yes</th>\n","    </tr>\n","    <tr>\n","      <th>date_served</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-01-01</th>\n","      <td>2.0</td>\n","      <td>189.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-01-02</th>\n","      <td>3.0</td>\n","      <td>247.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-01-03</th>\n","      <td>0.0</td>\n","      <td>220.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-01-04</th>\n","      <td>0.0</td>\n","      <td>168.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-01-05</th>\n","      <td>0.0</td>\n","      <td>160.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["is_correct_lang   No    Yes\n","date_served                \n","2018-01-01       2.0  189.0\n","2018-01-02       3.0  247.0\n","2018-01-03       0.0  220.0\n","2018-01-04       0.0  168.0\n","2018-01-05       0.0  160.0"]},"metadata":{"tags":[]},"execution_count":60}]},{"cell_type":"code","metadata":{"id":"Zbo9-KCg0kzd","colab_type":"code","outputId":"5a7175fa-0cd7-464a-ce3c-49e76935cb1e","executionInfo":{"status":"ok","timestamp":1575109434867,"user_tz":-480,"elapsed":734,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":578}},"source":["# Print results\n","print(language_check_df)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["is_correct_lang    No    Yes\n","date_served                 \n","2018-01-01        2.0  189.0\n","2018-01-02        3.0  247.0\n","2018-01-03        0.0  220.0\n","2018-01-04        0.0  168.0\n","2018-01-05        0.0  160.0\n","2018-01-06        1.0  151.0\n","2018-01-07        2.0  130.0\n","2018-01-08        0.0  154.0\n","2018-01-09        0.0  157.0\n","2018-01-10        0.0  170.0\n","2018-01-11       20.0  135.0\n","2018-01-12       18.0  130.0\n","2018-01-13       26.0  122.0\n","2018-01-14       20.0  131.0\n","2018-01-15       16.0  192.0\n","2018-01-16       28.0  127.0\n","2018-01-17       21.0  127.0\n","2018-01-18       31.0  121.0\n","2018-01-19       22.0  127.0\n","2018-01-20       28.0  124.0\n","2018-01-21       14.0  100.0\n","2018-01-22       13.0   72.0\n","2018-01-23       16.0   69.0\n","2018-01-24       13.0   83.0\n","2018-01-25       19.0   74.0\n","2018-01-26       24.0   92.0\n","2018-01-27       18.0  149.0\n","2018-01-28       28.0  136.0\n","2018-01-29       24.0  142.0\n","2018-01-30       23.0  145.0\n","2018-01-31       23.0  135.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5O9PdTc61sKr","colab_type":"text"},"source":["####Confirming house ads error\n","\n","- Add a `pct` column to `language_check_df` which divides the count where language is correct by the row sum extracted using the `.sum()` method.\n","- Make a line plot with the date as the x-axis and the `pct` column as the y-axis and show your results."]},{"cell_type":"code","metadata":{"id":"KsFGSFeV1zDf","colab_type":"code","outputId":"9154987d-98b1-47d3-e3b0-e8dafd31f80b","executionInfo":{"status":"ok","timestamp":1575109478114,"user_tz":-480,"elapsed":827,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":265}},"source":["# Divide the count where language is correct by the row sum\n","language_check_df['pct'] = language_check_df['Yes']/language_check_df.sum(axis=1)\n","\n","# Plot and show your results\n","plt.plot(language_check_df.index.values, language_check_df['pct'])\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZIAAAD4CAYAAADGmmByAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJZLc/Hbf3DVM/ZISlpir8MEYbAdJJEKkIqFEopS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KqZuUUi1KqVal1OfiXL9CKfWEUuoV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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"wPdMDpKN12lU","colab_type":"text"},"source":["####Resolving inconsistencies\n"]},{"cell_type":"markdown","metadata":{"id":"PZnNpBR614k3","colab_type":"text"},"source":["####Setting up conversion indexes\n","\n","Now that you've determined that language is, in fact, the issue with House Ads conversion, stakeholders need to know how many subscribers they lost as a result of this bug.\n","\n","In this exercise, you will index non-English language conversion rates against English conversion rates in the time period before the language bug arose."]},{"cell_type":"code","metadata":{"id":"M0Q38lTu1yws","colab_type":"code","outputId":"0ff77c10-5cc2-43a0-c734-0f60761e0844","executionInfo":{"status":"ok","timestamp":1575109523398,"user_tz":-480,"elapsed":774,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["# Calculate pre-error conversion rate\n","house_ads_bug = house_ads[house_ads['date_served'] < '2018-01-11']\n","lang_conv = conversion_rate(house_ads_bug, ['language_displayed']) \n","\n","# Index other language conversion rate against English\n","spanish_index = lang_conv['Spanish']/lang_conv['English']\n","arabic_index = lang_conv['Arabic']/lang_conv['English']\n","german_index = lang_conv['German']/lang_conv['English']\n","\n","print(\"Spanish index:\", spanish_index)\n","print(\"Arabic index:\", arabic_index)\n","print(\"German index:\", german_index)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Spanish index: 1.681924882629108\n","Arabic index: 5.045774647887324\n","German index: 4.485133020344287\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"PiELTZZ1pg60","colab_type":"text"},"source":["####Analyzing user preferences\n","\n","In this step, you will create a new DataFrame that you can perform calculations on to determine the expected number of subscribers. This DataFrame will include how many users prefer each language by day. \n","\n","Once you have the DataFrame, you can begin calculating how many subscribers you would have expected to have had the language bug not occurred."]},{"cell_type":"markdown","metadata":{"id":"diGpFbJlp2He","colab_type":"text"},"source":["- Group `house_ads` by `date_served` and `language_preferred`.\n","- Use a dictionary within a call to `.agg()` to calculate the number of unique users and sums the number of converted users.\n","- Unstack `converted` at `level = 1`"]},{"cell_type":"code","metadata":{"id":"E_qbm15s2AL2","colab_type":"code","colab":{}},"source":["# Group house_ads by date and language\n","converted = house_ads.groupby(['date_served', \n","                          'language_preferred'])\\\n","                        .agg({'user_id':'nunique',\n","                              'converted':'sum'})\n","\n","# Unstack converted\n","converted_df = pd.DataFrame(converted.unstack(level=1))"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4EKx6DYpqggo","colab_type":"text"},"source":["####Creating a DataFrame based on indexes\n","\n","An expected conversion DataFrame named `converted` has been created for you grouping `house_ads` by date and preferred language. It contains a count of unique users as well as the number of conversions for each language, each day.\n","\n","For example, you can access the number of Spanish-speaking users who received house ads using `converted[('user_id','Spanish')]`."]},{"cell_type":"markdown","metadata":{"id":"2BJf5pbWq2mh","colab_type":"text"},"source":["- Use `.loc` to create the column `english_conv_rate` in `converted` with the English conversion rate between `'2018-01-11'` and `'2018-01-31'`.\n","- Create expected conversion columns for each language by multiplying `english_conv_rate` by each language index (`spanish_index, arabic_index` or `german_index`).\n","- Multiply each language's expected conversion rate by the number of users who should have received house ads."]},{"cell_type":"code","metadata":{"id":"gjE_7CEkrBkv","colab_type":"code","colab":{}},"source":["# Create English conversion rate column for affected period\n","converted['english_conv_rate'] = converted.loc['2018-01-11':'2018-01-31'][('converted','English')]\n","\n","# Create expected conversion rates for each language\n","converted['expected_spanish_rate'] = converted['english_conv_rate']*spanish_index\n","converted['expected_arabic_rate'] = converted['english_conv_rate']*arabic_index\n","converted['expected_german_rate'] = converted['english_conv_rate']*german_index\n","\n","# Multiply number of users by the expected conversion rate\n","converted['expected_spanish_conv'] = converted['expected_spanish_rate']/100*converted[('user_id','Spanish')]\n","converted['expected_arabic_conv'] = converted['expected_arabic_rate']/100*converted[('user_id','Arabic')]\n","converted['expected_german_conv'] = converted['expected_german_rate']/100*converted[('user_id','German')]"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"urpqn25KrbUH","colab_type":"text"},"source":["####Assessing bug impact\n","It's time to calculate how many subscribers were lost due to mistakenly serving users English rather than their preferred language. \n"]},{"cell_type":"markdown","metadata":{"id":"onqoWVzvLNAw","colab_type":"text"},"source":["- Create the `converted` DataFrame by using `.loc` to select only rows where the date is between `'2018-01-11'` and `'2018-01-31'`.\n","- Sum the expected subscribers columns for each language in `converted` and store the results in `expected_subs`.\n","- Sum the actual subscribers for each language in `converted` and store the results in `actual_subs`.\n","- Subtract `actual_subs` from `expected_subs` to determine how many subscribers were lost due to the bug."]},{"cell_type":"code","metadata":{"id":"5TcW2KTtqoZp","colab_type":"code","colab":{}},"source":["# Use .loc to slice only the relevant dates\n","converted = converted.loc['2018-01-11':'2018-01-31']\n","\n","# Sum expected subscribers for each language\n","expected_subs = converted['expected_spanish_conv'].sum() + converted['expected_arabic_conv'].sum() + converted['expected_german_conv'].sum()\n","\n","# Calculate how many subscribers we actually got\n","actual_subs = converted[('converted','Spanish')].sum() + converted[('converted','Arabic')].sum() + converted[('converted','German')].sum()\n","\n","# Subtract how many subscribers we got despite the bug\n","lost_subs = expected_subs - actual_subs\n","print(lost_subs)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"a4-yYYJufkIT","colab_type":"text"},"source":["# 4. Personalization A/B Test\n","\n","In this chapter, you will analyze an A/B test and learn about the importance of segmentation when interpreting the results of a test."]},{"cell_type":"markdown","metadata":{"id":"dXUJVAqLLejq","colab_type":"text"},"source":["###A/B testing for marketing\n","\n","When running an A/B test when is the right time to determine key success metrics?\n","\n","\n","```\n","When designing the experiment.\n","```\n","\n"]},{"cell_type":"code","metadata":{"id":"GU1O5o7-1_2g","colab_type":"code","outputId":"b10cba6e-a714-4867-9f0b-92d07deabb7f","executionInfo":{"status":"ok","timestamp":1575350211084,"user_tz":-480,"elapsed":904,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":241}},"source":["marketing = pd.read_csv(\"marketing_new.csv\", index_col=0)\n","print(marketing.shape)\n","marketing.tail()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(10037, 15)\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>date_served</th>\n","      <th>marketing_channel</th>\n","      <th>variant</th>\n","      <th>converted</th>\n","      <th>language_displayed</th>\n","      <th>language_preferred</th>\n","      <th>age_group</th>\n","      <th>date_subscribed</th>\n","      <th>date_canceled</th>\n","      <th>subscribing_channel</th>\n","      <th>is_retained</th>\n","      <th>DoW</th>\n","      <th>channel_code</th>\n","      <th>is_correct_lang</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>10032</th>\n","      <td>a100007290</td>\n","      <td>2018-01-17</td>\n","      <td>Email</td>\n","      <td>control</td>\n","      <td>True</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>45-55 years</td>\n","      <td>2018-01-17</td>\n","      <td>2018-01-24</td>\n","      <td>Email</td>\n","      <td>False</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>10033</th>\n","      <td>a100007291</td>\n","      <td>2018-01-17</td>\n","      <td>Email</td>\n","      <td>control</td>\n","      <td>True</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>55+ years</td>\n","      <td>2018-01-17</td>\n","      <td>NaN</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>10034</th>\n","      <td>a100007292</td>\n","      <td>2018-01-05</td>\n","      <td>Instagram</td>\n","      <td>control</td>\n","      <td>False</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>55+ years</td>\n","      <td>2018-01-17</td>\n","      <td>NaN</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>10035</th>\n","      <td>a100007293</td>\n","      <td>2018-01-17</td>\n","      <td>Email</td>\n","      <td>control</td>\n","      <td>True</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-17</td>\n","      <td>NaN</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>10036</th>\n","      <td>a100007294</td>\n","      <td>2018-01-06</td>\n","      <td>Facebook</td>\n","      <td>control</td>\n","      <td>False</td>\n","      <td>German</td>\n","      <td>German</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-17</td>\n","      <td>NaN</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          user_id date_served  ... channel_code is_correct_lang\n","10032  a100007290  2018-01-17  ...          4.0             Yes\n","10033  a100007291  2018-01-17  ...          4.0             Yes\n","10034  a100007292  2018-01-05  ...          4.0             Yes\n","10035  a100007293  2018-01-17  ...          4.0             Yes\n","10036  a100007294  2018-01-06  ...          4.0             Yes\n","\n","[5 rows x 15 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"50CxbJ2NMT8B","colab_type":"text"},"source":["- Isolate the rows of `marketing` where the `'marketing_channel'` is `'Email'` and store the results in `email`.\n","- Group `marketing` by variant and sum the unique users and store the results in `alloc`.\n","- Plot the results of `alloc` in a bar chart."]},{"cell_type":"code","metadata":{"id":"o7qNS4DzMbH0","colab_type":"code","outputId":"f0d0cce5-e436-4546-89ae-842d77c77628","executionInfo":{"status":"ok","timestamp":1575350346879,"user_tz":-480,"elapsed":1164,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":362}},"source":["# Subset the DataFrame\n","email = marketing[marketing['marketing_channel'] == 'Email']\n","\n","# Group the email DataFrame by variant \n","alloc = email.groupby(['variant'])['user_id'].nunique()\n","\n","# Plot a bar chart of the test allocation\n","alloc.plot(kind='bar')\n","plt.title('Personalization test allocation')\n","plt.ylabel('# participants')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAFZCAYAAAB33zMcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1DIUJEmt/wK1l2TGKmAcCgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"4duMZrgsN_lN","colab_type":"text"},"source":["####Comparing conversion rates\n","\n","Now that we know allocation is relatively even let's look at the conversion rate for the control and personalization. Since we chose conversion rate as our key metrics for this test, it is highly important that we evaluate whether or not conversion was higher in the personalization treatment compared with the control. "]},{"cell_type":"code","metadata":{"id":"ZZUA9m4sOQ1Z","colab_type":"code","outputId":"c7b14a17-9527-4b8b-ec41-cec0923970b0","executionInfo":{"status":"ok","timestamp":1575350772630,"user_tz":-480,"elapsed":1149,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["email.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>date_served</th>\n","      <th>marketing_channel</th>\n","      <th>variant</th>\n","      <th>converted</th>\n","      <th>language_displayed</th>\n","      <th>language_preferred</th>\n","      <th>age_group</th>\n","      <th>date_subscribed</th>\n","      <th>date_canceled</th>\n","      <th>subscribing_channel</th>\n","      <th>is_retained</th>\n","      <th>DoW</th>\n","      <th>channel_code</th>\n","      <th>is_correct_lang</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>354</th>\n","      <td>a100000526</td>\n","      <td>2018-01-01</td>\n","      <td>Email</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>Arabic</td>\n","      <td>Arabic</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaN</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>358</th>\n","      <td>a100000530</td>\n","      <td>2018-01-05</td>\n","      <td>Email</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>Arabic</td>\n","      <td>Arabic</td>\n","      <td>19-24 years</td>\n","      <td>2018-01-05</td>\n","      <td>NaN</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>362</th>\n","      <td>a100000534</td>\n","      <td>2018-01-09</td>\n","      <td>Email</td>\n","      <td>personalization</td>\n","      <td>False</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>45-55 years</td>\n","      <td>2018-01-09</td>\n","      <td>NaN</td>\n","      <td>Email</td>\n","      <td>True</td>\n","      <td>1.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>366</th>\n","      <td>a100000538</td>\n","      <td>2018-01-13</td>\n","      <td>Email</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>Arabic</td>\n","      <td>Arabic</td>\n","      <td>24-30 years</td>\n","      <td>2018-01-13</td>\n","      <td>2018-01-23</td>\n","      <td>Email</td>\n","      <td>False</td>\n","      <td>5.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>370</th>\n","      <td>a100000542</td>\n","      <td>2018-01-17</td>\n","      <td>Email</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-17</td>\n","      <td>2018-02-12</td>\n","      <td>Email</td>\n","      <td>False</td>\n","      <td>2.0</td>\n","      <td>4.0</td>\n","      <td>Yes</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        user_id date_served  ... channel_code is_correct_lang\n","354  a100000526  2018-01-01  ...          4.0             Yes\n","358  a100000530  2018-01-05  ...          4.0             Yes\n","362  a100000534  2018-01-09  ...          4.0             Yes\n","366  a100000538  2018-01-13  ...          4.0             Yes\n","370  a100000542  2018-01-17  ...          4.0             Yes\n","\n","[5 rows x 15 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"vy_xXUZGOJlW","colab_type":"text"},"source":["- Group the `email` DataFrame by `user_id` and `variant` while selecting the maximum value of the `converted` column and store the results in `subscribers`.\n","- Drop missing values from the `control` column of `subscribers_df`.\n","- Drop missing values from the `personalization` column of `subscribers_df`.\n","- Calculate the conversion rate for both `personalization` and `control` using the appropriate function for each."]},{"cell_type":"code","metadata":{"id":"fGaEPzIHOWqA","colab_type":"code","outputId":"1eb3dc05-2054-4b8f-82c8-bc8cb87498d2","executionInfo":{"status":"ok","timestamp":1575350822866,"user_tz":-480,"elapsed":1232,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Group marketing by user_id and variant\n","subscribers = email.groupby(['user_id', \n","                             'variant'])['converted'].max()\n","subscribers_df = pd.DataFrame(subscribers.unstack(level=1)) \n","\n","# Drop missing values from the control column\n","control = subscribers_df['control'].dropna()\n","\n","# Drop missing values from the personalization column\n","personalization = subscribers_df['personalization'].dropna()\n","\n","print('Control conversion rate:', np.mean(control))\n","print('Personalization conversion rate:', np.mean(personalization))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Control conversion rate: 0.2814814814814815\n","Personalization conversion rate: 0.3908450704225352\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7eoiWaqFfmAC","colab_type":"code","outputId":"9bf21c67-55fb-45be-bdd2-016521ef556e","executionInfo":{"status":"ok","timestamp":1575350832604,"user_tz":-480,"elapsed":898,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["subscribers.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["user_id     variant        \n","a100000526  personalization     True\n","a100000530  personalization     True\n","a100000534  personalization    False\n","a100000538  personalization     True\n","a100000542  personalization     True\n","Name: converted, dtype: bool"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"fuuqcJtFOhCP","colab_type":"code","outputId":"a2bb7ea2-67f8-4472-c418-2a57f93e0708","executionInfo":{"status":"ok","timestamp":1575350853346,"user_tz":-480,"elapsed":1076,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":235}},"source":["subscribers_df[:5]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>variant</th>\n","      <th>control</th>\n","      <th>personalization</th>\n","    </tr>\n","    <tr>\n","      <th>user_id</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>a100000526</th>\n","      <td>NaN</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>a100000530</th>\n","      <td>NaN</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>a100000534</th>\n","      <td>NaN</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>a100000538</th>\n","      <td>NaN</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>a100000542</th>\n","      <td>NaN</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["variant    control personalization\n","user_id                           \n","a100000526     NaN            True\n","a100000530     NaN            True\n","a100000534     NaN           False\n","a100000538     NaN            True\n","a100000542     NaN            True"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"53DLko_IPGiO","colab_type":"text"},"source":["###Calculating lift & significance testing\n"]},{"cell_type":"markdown","metadata":{"id":"jnxndwq3PH_L","colab_type":"text"},"source":["####Creating a lift function\n","\n","Lift can be calculated by calculating the difference between the treatment effect (or the mean) of the treatment compared to the treatment effect of the control divided by the treatment effect of the control. \n","\n","The result is the percent difference between the control and treatment."]},{"cell_type":"markdown","metadata":{"id":"1KxDktEwPRJF","colab_type":"text"},"source":["- Calculate the mean of `a` and `b` using `np.mean()`.\n","- Use `a_mean` and `b_mean` to calculate the lift of the treatment.\n","- Print the results of the `lift()` function you created using the `control` and `personalization` variables."]},{"cell_type":"code","metadata":{"id":"dZGbD02rPXsf","colab_type":"code","outputId":"b9ba22e9-7789-4702-ffdf-d5b12ab6aaa9","executionInfo":{"status":"ok","timestamp":1575351075876,"user_tz":-480,"elapsed":1137,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["def lift(a,b):\n","    # Calcuate the mean of a and b \n","    a_mean = np.mean(a)\n","    b_mean = np.mean(b)\n","    \n","    # Calculate the lift using a_mean and b_mean\n","    lift = (b_mean-a_mean)/a_mean\n","  \n","    return str(round(lift*100, 2)) + '%'\n","  \n","# Print lift() with control and personalization as inputs\n","print(lift(control, personalization))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["38.85%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mR0HxGqfPnjN","colab_type":"text"},"source":["###A/B testing & segmentation\n"]},{"cell_type":"markdown","metadata":{"id":"yecKxHeoPpn8","colab_type":"text"},"source":["####Building an A/B test segmenting function\n","\n","Since you want to segment our data multiple times, you will build a function `ab_segmentation()` that analyzes the impact of your A/B tests on segments of data that you can reuse each time you want to conduct this kind of analysis.\n","\n","Your function will take in a column name and run through each unique value in that column calculating lift and statistical significance."]},{"cell_type":"markdown","metadata":{"id":"P1df3wSrPzIt","colab_type":"text"},"source":["- Build a for loop within our `ab_segmentation()` function that runs through each unique value in the user-inputted column name `segment`.\n","- Isolate the rows in marketing where the marketing channel is `'Email'` and the user-inputted column `segment` equals `subsegment`.\n","- Print the results of the `lift()` and `stats.ttest_ind()` functions."]},{"cell_type":"code","metadata":{"id":"n1HZIYCvQFzv","colab_type":"code","outputId":"938f0b17-e329-4f88-fe6d-88d3bfa2944e","executionInfo":{"status":"ok","timestamp":1575351254058,"user_tz":-480,"elapsed":1203,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["marketing.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>user_id</th>\n","      <th>date_served</th>\n","      <th>marketing_channel</th>\n","      <th>variant</th>\n","      <th>converted</th>\n","      <th>language_displayed</th>\n","      <th>language_preferred</th>\n","      <th>age_group</th>\n","      <th>date_subscribed</th>\n","      <th>date_canceled</th>\n","      <th>subscribing_channel</th>\n","      <th>is_retained</th>\n","      <th>DoW</th>\n","      <th>channel_code</th>\n","      <th>is_correct_lang</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>a100000029</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>0-18 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaN</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>a100000030</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>19-24 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaN</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>a100000031</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>24-30 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaN</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>a100000032</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>30-36 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaN</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>a100000033</td>\n","      <td>2018-01-01</td>\n","      <td>House Ads</td>\n","      <td>personalization</td>\n","      <td>True</td>\n","      <td>English</td>\n","      <td>English</td>\n","      <td>36-45 years</td>\n","      <td>2018-01-01</td>\n","      <td>NaN</td>\n","      <td>House Ads</td>\n","      <td>True</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Yes</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      user_id date_served marketing_channel  ...  DoW  channel_code is_correct_lang\n","0  a100000029  2018-01-01         House Ads  ...  0.0           1.0             Yes\n","1  a100000030  2018-01-01         House Ads  ...  0.0           1.0             Yes\n","2  a100000031  2018-01-01         House Ads  ...  0.0           1.0             Yes\n","3  a100000032  2018-01-01         House Ads  ...  0.0           1.0             Yes\n","4  a100000033  2018-01-01         House Ads  ...  0.0           1.0             Yes\n","\n","[5 rows x 15 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"2P1RnqmLQ1OV","colab_type":"code","colab":{}},"source":["from scipy import stats"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"k3zLrPhSP8NK","colab_type":"code","colab":{}},"source":["def ab_segmentation(segment):\n","  # Build a for loop for each segment in marketing\n","  for subsegment in np.unique(marketing[segment].values):\n","      print(subsegment)\n","      \n","      # Limit marketing to email and subsegment      \n","      email = marketing[(marketing['marketing_channel'] == 'Email') & (marketing[segment] == subsegment)]\n","\n","      subscribers = email.groupby(['user_id', 'variant'])['converted'].max()\n","      subscribers = pd.DataFrame(subscribers.unstack(level=1)) \n","      control = subscribers['control'].dropna()\n","      personalization = subscribers['personalization'].dropna()\n","\n","      print('lift:', lift(control, personalization))\n","      print('t-statistic:', stats.ttest_ind(control, personalization), '\\n\\n')"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MdHxlMPFQpEc","colab_type":"text"},"source":["- Run our `ab_segmentation()` function on `'language_displayed'`."]},{"cell_type":"code","metadata":{"id":"3ZotfsQoOhFL","colab_type":"code","outputId":"f129e3cb-02cc-4458-9838-f3aab6dde0d8","executionInfo":{"status":"ok","timestamp":1575351450174,"user_tz":-480,"elapsed":902,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["# Use ab_segmentation on language displayed\n","ab_segmentation('language_displayed')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Arabic\n","lift: 50.0%\n","t-statistic: Ttest_indResult(statistic=-0.5773502691896255, pvalue=0.5795840000000001) \n","\n","\n","English\n","lift: 39.0%\n","t-statistic: Ttest_indResult(statistic=-2.2183598646203166, pvalue=0.026991701290720815) \n","\n","\n","German\n","lift: -1.62%\n","t-statistic: Ttest_indResult(statistic=0.1910083418078718, pvalue=0.8494394170062678) \n","\n","\n","Spanish\n","lift: 166.67%\n","t-statistic: Ttest_indResult(statistic=-2.3570226039551585, pvalue=0.040156718110477524) \n","\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"F3-u8dZbQwiv","colab_type":"text"},"source":["- Run our `ab_segmentation()` function on `'age_group'`."]},{"cell_type":"code","metadata":{"id":"AktM1Z7NOhAV","colab_type":"code","outputId":"6bb2d87b-2bc1-4a30-cfe8-9f10312d370c","executionInfo":{"status":"ok","timestamp":1575351482502,"user_tz":-480,"elapsed":1002,"user":{"displayName":"Zhenna LU","photoUrl":"","userId":"13596549313510787594"}},"colab":{"base_uri":"https://localhost:8080/","height":612}},"source":["# Use ab_segmentation on age group\n","ab_segmentation('age_group')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0-18 years\n","lift: 121.4%\n","t-statistic: Ttest_indResult(statistic=-2.966044912142211, pvalue=0.0038724494391297226) \n","\n","\n","19-24 years\n","lift: 106.24%\n","t-statistic: Ttest_indResult(statistic=-3.03179438478667, pvalue=0.0030623836114689134) \n","\n","\n","24-30 years\n","lift: 161.19%\n","t-statistic: Ttest_indResult(statistic=-3.861539544326876, pvalue=0.00018743381094867337) \n","\n","\n","30-36 years\n","lift: -100.0%\n","t-statistic: Ttest_indResult(statistic=3.1859064644147996, pvalue=0.0023238487431765137) \n","\n","\n","36-45 years\n","lift: -85.23%\n","t-statistic: Ttest_indResult(statistic=2.4317901279318503, pvalue=0.01797568600978829) \n","\n","\n","45-55 years\n","lift: -72.22%\n","t-statistic: Ttest_indResult(statistic=2.065499127317933, pvalue=0.043062339688201196) \n","\n","\n","55+ years\n","lift: -100.0%\n","t-statistic: Ttest_indResult(statistic=3.3265654564203397, pvalue=0.0016358623456360435) \n","\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"93PKXQdNQ80-","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ES2BJ_CgQ8qX","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}