5 - Pandas - Reading CSV and Basic Plotting.ipynb

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    "![rmotr](https://user-images.githubusercontent.com/7065401/52071918-bda15380-2562-11e9-828c-7f95297e4a82.png)\n",
    "<hr style=\"margin-bottom: 40px;\">\n",
    "\n",
    "<img src=\"https://user-images.githubusercontent.com/7065401/75165824-badf4680-5701-11ea-9c5b-5475b0a33abf.png\"\n",
    "    style=\"width:300px; float: right; margin: 0 40px 40px 40px;\"></img>\n",
    "\n",
    "# Reading external data & Plotting\n",
    "\n",
    "[Source](https://blockchain.info/charts/market-price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Hands on! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can easily read data stored in different file formats like CSV, JSON, XML or even Excel. Parsing always involves specifying the correct structure, encoding and other details. The `read_csv` method reads CSV files and accepts many parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/btc-market-price.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "   2017-04-02 00:00:00  1099.169125\n",
       "0  2017-04-03 00:00:00  1141.813000\n",
       "1  2017-04-04 00:00:00  1141.600363\n",
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       "3  2017-04-06 00:00:00  1196.307937\n",
       "4  2017-04-07 00:00:00  1190.454250"
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   "source": [
    "df.head()"
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   "metadata": {},
   "source": [
    "The CSV file we're reading has only two columns: `timestamp` and `price`. It doesn't have a header, it contains whitespaces and has values separated by commas. pandas automatically assigned the first row of data as headers, which is incorrect. We can overwrite this behavior with the `header` parameter:"
   ]
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/btc-market-price.csv', header=None)"
   ]
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   "execution_count": 11,
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       "                     0            1\n",
       "0  2017-04-02 00:00:00  1099.169125\n",
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    "df.head()"
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   "source": [
    "We can then set the names of each column explicitely by setting the `df.columns` attribute:"
   ]
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   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "df.columns = ['Timestamp', 'Price']"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
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    {
     "data": {
      "text/plain": [
       "(365, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.shape"
   ]
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   "execution_count": 13,
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       "      <th>Timestamp</th>\n",
       "      <th>Price</th>\n",
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       "             Timestamp        Price\n",
       "0  2017-04-02 00:00:00  1099.169125\n",
       "1  2017-04-03 00:00:00  1141.813000\n",
       "2  2017-04-04 00:00:00  1141.600363\n",
       "3  2017-04-05 00:00:00  1133.079314\n",
       "4  2017-04-06 00:00:00  1196.307937"
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    "df.head()"
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       "      <th>Timestamp</th>\n",
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       "      <th>362</th>\n",
       "      <td>2018-03-30 00:00:00</td>\n",
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       "      <th>363</th>\n",
       "      <td>2018-03-31 00:00:00</td>\n",
       "      <td>6935.480000</td>\n",
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       "      <th>364</th>\n",
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      "text/plain": [
       "               Timestamp        Price\n",
       "362  2018-03-30 00:00:00  6882.531667\n",
       "363  2018-03-31 00:00:00  6935.480000\n",
       "364  2018-04-01 00:00:00  6794.105000"
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   ],
   "source": [
    "df.tail(3)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The type of the `Price` column was correctly interpreted as `float`, but the `Timestamp` was interpreted as a regular string (`object` in pandas notation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp     object\n",
       "Price        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can perform a vectorized operation to parse all the Timestamp values as `Datetime` objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-04-02\n",
       "1   2017-04-03\n",
       "2   2017-04-04\n",
       "3   2017-04-05\n",
       "4   2017-04-06\n",
       "Name: Timestamp, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(df['Timestamp']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Timestamp'] = pd.to_datetime(df['Timestamp'])"
   ]
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      "text/plain": [
       "   Timestamp        Price\n",
       "0 2017-04-02  1099.169125\n",
       "1 2017-04-03  1141.813000\n",
       "2 2017-04-04  1141.600363\n",
       "3 2017-04-05  1133.079314\n",
       "4 2017-04-06  1196.307937"
      ]
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     "execution_count": 21,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp    datetime64[ns]\n",
       "Price               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The timestamp looks a lot like the index of this `DataFrame`: `date > price`. We can change the autoincremental ID generated by pandas and use the `Timestamp DS` column as the Index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Timestamp', inplace=True)"
   ]
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      "text/plain": [
       "                  Price\n",
       "Timestamp              \n",
       "2017-04-02  1099.169125\n",
       "2017-04-03  1141.813000\n",
       "2017-04-04  1141.600363\n",
       "2017-04-05  1133.079314\n",
       "2017-04-06  1196.307937"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price    4193.574667\n",
       "Name: 2017-09-29 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2017-09-29']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Putting everything together\n",
    "\n",
    "And now, we've finally arrived to the final, desired version of the `DataFrame` parsed from our CSV file. The steps were:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/btc-market-price.csv', header=None)\n",
    "df.columns = ['Timestamp', 'Price']\n",
    "df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n",
    "df.set_index('Timestamp', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>1099.169125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>1141.813000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>1141.600363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>1133.079314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>1196.307937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price\n",
       "Timestamp              \n",
       "2017-04-02  1099.169125\n",
       "2017-04-03  1141.813000\n",
       "2017-04-04  1141.600363\n",
       "2017-04-05  1133.079314\n",
       "2017-04-06  1196.307937"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There should be a better way**. And there is 😎. And there usually is, explicitly with all these repetitive tasks with pandas.\n",
    "\n",
    "The `read_csv` function is extremely powerful and you can specify many more parameters at import time. We can achive the same results with only one line by doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    'data/btc-market-price.csv',\n",
    "    header=None,\n",
    "    names=['Timestamp', 'Price'],\n",
    "    index_col=0,\n",
    "    parse_dates=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>1099.169125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>1141.813000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>1141.600363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>1133.079314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>1196.307937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price\n",
       "Timestamp              \n",
       "2017-04-02  1099.169125\n",
       "2017-04-03  1141.813000\n",
       "2017-04-04  1141.600363\n",
       "2017-04-05  1133.079314\n",
       "2017-04-06  1196.307937"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price    4193.574667\n",
       "Name: 2017-09-29 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2017-09-29']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Plotting basics\n",
    "\n",
    "`pandas` integrates with Matplotlib and creating a plot is as simple as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc299665e0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Behind the scenes, it's using `matplotlib.pyplot`'s interface. We can create a similar plot with the `plt.plot()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efc29a8c700>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.index, df['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plt.plot()` accepts many parameters, but the first two ones are the most important ones: the values for the `X` and `Y` axes. Another example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(-10, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7efc299abf10>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're using `matplotlib`'s global API, which is horrible but it's the most popular one. We'll learn later how to use the _OOP_ API which will make our work much easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.plot(x, x ** 2)\n",
    "plt.plot(x, -1 * (x ** 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `plt` function alters the global state. If you want to set settings of your plot you can use the `plt.figure` function. Others like `plt.title` keep altering the global plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(x, x ** 2)\n",
    "plt.plot(x, -1 * (x ** 2))\n",
    "\n",
    "plt.title('My Nice Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the arguments in `plt.figure` and `plt.plot` are available in the pandas' `plot` interface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.plot(figsize=(16, 9), title='Bitcoin Price 2017-2018')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## A more challenging parsing\n",
    "\n",
    "To demonstrate plotting two columns together, we'll try to add Ether prices to our `df` DataFrame. The ETH prices data can be found in the `data/eth-price.csv` file. The problem is that it seems like that CSV file was created by someone who really hated programmers. Take a look at it and see how ugly it looks like. We'll still use `pandas` to parse it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date(UTC)</th>\n",
       "      <th>UnixTimeStamp</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4/2/2017</td>\n",
       "      <td>1491091200</td>\n",
       "      <td>48.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/3/2017</td>\n",
       "      <td>1491177600</td>\n",
       "      <td>44.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/4/2017</td>\n",
       "      <td>1491264000</td>\n",
       "      <td>44.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/5/2017</td>\n",
       "      <td>1491350400</td>\n",
       "      <td>44.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/6/2017</td>\n",
       "      <td>1491436800</td>\n",
       "      <td>43.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date(UTC)  UnixTimeStamp  Value\n",
       "0  4/2/2017     1491091200  48.55\n",
       "1  4/3/2017     1491177600  44.13\n",
       "2  4/4/2017     1491264000  44.43\n",
       "3  4/5/2017     1491350400  44.90\n",
       "4  4/6/2017     1491436800  43.23"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth = pd.read_csv('data/eth-price.csv')\n",
    "\n",
    "eth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, it has a `Value` column (which represents the price), a `Date(UTC)` one that has a string representing dates and also a `UnixTimeStamp` date represeting the datetime in unix timestamp format. The header is read automatically, let's try to parse dates with the CSV Reader:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date(UTC)         object\n",
      "UnixTimeStamp      int64\n",
      "Value            float64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
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       "      <th>Date(UTC)</th>\n",
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       "      <th>Value</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>4/2/2017</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/3/2017</td>\n",
       "      <td>1491177600</td>\n",
       "      <td>44.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/4/2017</td>\n",
       "      <td>1491264000</td>\n",
       "      <td>44.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/5/2017</td>\n",
       "      <td>1491350400</td>\n",
       "      <td>44.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/6/2017</td>\n",
       "      <td>1491436800</td>\n",
       "      <td>43.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date(UTC)  UnixTimeStamp  Value\n",
       "0  4/2/2017     1491091200  48.55\n",
       "1  4/3/2017     1491177600  44.13\n",
       "2  4/4/2017     1491264000  44.43\n",
       "3  4/5/2017     1491350400  44.90\n",
       "4  4/6/2017     1491436800  43.23"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth = pd.read_csv('data/eth-price.csv', parse_dates=True)\n",
    "\n",
    "print(eth.dtypes)\n",
    "eth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like the `parse_dates` attribute didn't work. We'll need to add a little bit more customization. Let's divide this problem and focus on the problem of \"date parsing\" first. The simplest option would be to use the `UnixTimeStamp` column. The `pandas` module has a `to_datetime` function that converts Unix timestamps to Datetime objects automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   1970-01-01 00:00:01.491091200\n",
       "1   1970-01-01 00:00:01.491177600\n",
       "2   1970-01-01 00:00:01.491264000\n",
       "3   1970-01-01 00:00:01.491350400\n",
       "4   1970-01-01 00:00:01.491436800\n",
       "Name: UnixTimeStamp, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(eth['UnixTimeStamp']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem is the precision of unix timestamps. To match both columns we'll need to use the same index and, our `df` containing Bitcoin prices, is \"per day\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>1099.169125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>1141.813000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>1141.600363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>1133.079314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>1196.307937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price\n",
       "Timestamp              \n",
       "2017-04-02  1099.169125\n",
       "2017-04-03  1141.813000\n",
       "2017-04-04  1141.600363\n",
       "2017-04-05  1133.079314\n",
       "2017-04-06  1196.307937"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could either, remove the precision of `UnixTimeStamp` or attempt to parse the `Date(UTC)`. Let's do String parsing of `Date(UTC)` for fun:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-04-02\n",
       "1   2017-04-03\n",
       "2   2017-04-04\n",
       "3   2017-04-05\n",
       "4   2017-04-06\n",
       "Name: Date(UTC), dtype: datetime64[ns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(eth['Date(UTC)']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That seems to work fine! Why isn't it then parsing the `Date(UTC)` column? Simple, the `parse_dates=True` parameter will instruct pandas to parse the index of the `DataFrame`. If you want to parse any other column, you must explicitly pass the column position or name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date(UTC)</th>\n",
       "      <th>UnixTimeStamp</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>1491091200</td>\n",
       "      <td>48.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>1491177600</td>\n",
       "      <td>44.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>1491264000</td>\n",
       "      <td>44.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>1491350400</td>\n",
       "      <td>44.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>1491436800</td>\n",
       "      <td>43.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date(UTC)  UnixTimeStamp  Value\n",
       "0 2017-04-02     1491091200  48.55\n",
       "1 2017-04-03     1491177600  44.13\n",
       "2 2017-04-04     1491264000  44.43\n",
       "3 2017-04-05     1491350400  44.90\n",
       "4 2017-04-06     1491436800  43.23"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/eth-price.csv', parse_dates=[0]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting everything together again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 362 entries, 2017-04-02 to 2018-04-01\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   UnixTimeStamp  362 non-null    int64  \n",
      " 1   Value          362 non-null    float64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 8.5 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>UnixTimeStamp</th>\n",
       "      <th>Value</th>\n",
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       "    <tr>\n",
       "      <th>Date(UTC)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>1491091200</td>\n",
       "      <td>48.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>1491177600</td>\n",
       "      <td>44.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>1491264000</td>\n",
       "      <td>44.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>1491350400</td>\n",
       "      <td>44.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>1491436800</td>\n",
       "      <td>43.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            UnixTimeStamp  Value\n",
       "Date(UTC)                       \n",
       "2017-04-02     1491091200  48.55\n",
       "2017-04-03     1491177600  44.13\n",
       "2017-04-04     1491264000  44.43\n",
       "2017-04-05     1491350400  44.90\n",
       "2017-04-06     1491436800  43.23"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth = pd.read_csv('data/eth-price.csv', parse_dates=True, index_col=0)\n",
    "print(eth.info())\n",
    "\n",
    "eth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now combine both `DataFrame`s into one. Both have the same index, so aligning both prices will be easy. Let's first create an empty `DataFrame` and with the index from Bitcoin prices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = pd.DataFrame(index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [2017-04-02 00:00:00, 2017-04-03 00:00:00, 2017-04-04 00:00:00, 2017-04-05 00:00:00, 2017-04-06 00:00:00]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can now just set columns from the other `DataFrame`s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices['Bitcoin'] = df['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices['Ether'] = eth['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Bitcoin</th>\n",
       "      <th>Ether</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-02</th>\n",
       "      <td>1099.169125</td>\n",
       "      <td>48.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03</th>\n",
       "      <td>1141.813000</td>\n",
       "      <td>44.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-04</th>\n",
       "      <td>1141.600363</td>\n",
       "      <td>44.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>1133.079314</td>\n",
       "      <td>44.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>1196.307937</td>\n",
       "      <td>43.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Bitcoin  Ether\n",
       "Timestamp                     \n",
       "2017-04-02  1099.169125  48.55\n",
       "2017-04-03  1141.813000  44.13\n",
       "2017-04-04  1141.600363  44.43\n",
       "2017-04-05  1133.079314  44.90\n",
       "2017-04-06  1196.307937  43.23"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now try plotting both values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc298f7ca0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.plot(figsize=(12, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔seems like there's a tiny gap between Dec 2017 and Jan 2018. Let's zoom in there:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efc297fb040>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.loc['2017-12-01':'2018-01-01'].plot(figsize=(12, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh no, missing data 😱. We'll learn how to deal with that later 😉.\n",
    "\n",
    "Btw, did you note that fancy indexing `'2017-12-01':'2018-01-01'` 😏. That's pandas power 💪. We'll learn how to deal with TimeSeries later too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}