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170 changes: 170 additions & 0 deletions jupyter_notebooks/076_How_do_you_create_a_frequency_table_from_a_DataFrame_column.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "30ebb570",
"metadata": {},
"source": [
"# How do you create a frequency table from a DataFrame column?"
]
},
{
"cell_type": "markdown",
"id": "30ca9272",
"metadata": {},
"source": [
"**Question:**\n",
"\n",
"How do you create a frequency table from a DataFrame column in pandas?\n",
"\n",
"\n",
"\n",
"---\n",
"\n",
"\n",
"\n",
"**Creating a Frequency Table from a DataFrame Column in Pandas**\n",
"\n",
"\n",
"\n",
"A frequency table, also known as a count table, is a valuable tool in data analysis that summarizes the count of unique values in a dataset. In this tutorial, we'll explore how to create a frequency table from a DataFrame column using pandas, a powerful data manipulation library in Python.\n",
"\n",
"\n",
"\n",
"**Introduction**\n",
"\n",
"\n",
"\n",
"Frequency tables provide insights into the distribution of categorical or discrete variables within a dataset. They allow us to understand the frequency or occurrence of each unique value in a column, which is essential for exploratory data analysis and understanding the characteristics of the data.\n",
"\n",
"\n",
"\n",
"**Loading the Titanic Dataset**\n",
"\n",
"\n",
"\n",
"Before we dive into creating a frequency table, let's load the Titanic dataset, which contains information about passengers aboard the Titanic. We'll use this dataset to demonstrate how to create a frequency table."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "962444b0",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import pandas as pd\n",
"\n",
"\n",
"\n",
"# Load the Titanic dataset\n",
"\n",
"url = \"https://raw.githubusercontent.com/moscolitos/titanic-dataset/main/Titanic-Dataset.csv\"\n",
"\n",
"titanic_data = pd.read_csv(url)\n",
"\n",
"\n",
"\n",
"# Display the first few rows of the dataset\n",
"\n",
"print(titanic_data.head())"
]
},
{
"cell_type": "markdown",
"id": "30c4363f",
"metadata": {},
"source": [
"**Creating a Frequency Table**\n",
"\n",
"\n",
"\n",
"To create a frequency table from a DataFrame column, we can use the `value_counts()` function in pandas."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f5c2a01",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Create a frequency table for the 'Sex' column\n",
"\n",
"sex_frequency = titanic_data['Sex'].value_counts()\n",
"\n",
"\n",
"\n",
"print(\"Frequency Table for Sex Column:\")\n",
"\n",
"print(sex_frequency)"
]
},
{
"cell_type": "markdown",
"id": "fd0b53f4",
"metadata": {},
"source": [
"In this code snippet, we use the `value_counts()` function on the 'Sex' column of the DataFrame `titanic_data` to generate the frequency table. This function returns a Series object with the count of unique values in the column.\n",
"\n",
"\n",
"\n",
"**Understanding the Parameters**\n",
"\n",
"\n",
"\n",
"The `value_counts()` function has several optional parameters that allow us to customize the behavior of the frequency table:\n",
"\n",
"\n",
"\n",
"- `normalize`: If set to `True`, returns the relative frequencies instead of counts.\n",
"\n",
"- `sort`: If set to `True`, sorts the results by frequencies in descending order.\n",
"\n",
"- `ascending`: If set to `True`, sorts the results in ascending order.\n",
"\n",
"- `bins`: For numeric data, divides the data into discrete bins and counts the occurrences in each bin."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2ea4181",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Create a frequency table with normalized values\n",
"\n",
"sex_frequency_normalized = titanic_data['Sex'].value_counts(normalize=True)\n",
"\n",
"\n",
"\n",
"print(\"Normalized Frequency Table for Sex Column:\")\n",
"\n",
"print(sex_frequency_normalized)"
]
},
{
"cell_type": "markdown",
"id": "ad86577d",
"metadata": {},
"source": [
"In this example, we use the `normalize=True` parameter to obtain relative frequencies instead of counts.\n",
"\n",
"\n",
"\n",
"**Conclusion**\n",
"\n",
"\n",
"\n",
"In this tutorial, we learned how to create a frequency table from a DataFrame column in pandas. We used the Titanic dataset to demonstrate the process and introduced the `value_counts()` function, which is instrumental in generating frequency tables. Frequency tables provide valuable insights into the distribution of categorical variables, aiding in data exploration and analysis."
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
143 changes: 143 additions & 0 deletions jupyter_notebooks/077_How_do_you_melt_a_DataFrame_into_a_long_format.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "c9fd4d5d",
"metadata": {},
"source": [
"# How do you melt a DataFrame into a long format?"
]
},
{
"cell_type": "markdown",
"id": "b265b6bd",
"metadata": {},
"source": [
"**Question:**\n",
"\n",
"How do you melt a DataFrame into a long format in pandas?\n",
"\n",
"\n",
"\n",
"---\n",
"\n",
"\n",
"\n",
"**Melting a DataFrame into a Long Format in Pandas**\n",
"\n",
"\n",
"\n",
"Data often comes in various formats, and transforming it into a format suitable for analysis is a common task in data preprocessing. In this tutorial, we'll explore how to melt a DataFrame into a long format using pandas, a versatile data manipulation library in Python.\n",
"\n",
"\n",
"\n",
"**Introduction**\n",
"\n",
"\n",
"\n",
"The process of melting, also known as unpivoting or reshaping, involves transforming a DataFrame from a wide format to a long format. This transformation is useful when we want to analyze data in a format where each row represents a single observation, making it easier to perform operations such as aggregation and visualization.\n",
"\n",
"\n",
"\n",
"**Loading the Titanic Dataset**\n",
"\n",
"\n",
"\n",
"Before we delve into melting a DataFrame, let's load the Titanic dataset, which contains information about passengers aboard the Titanic. We'll use this dataset to demonstrate how to melt a DataFrame."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d98a1dd7",
"metadata": {},
"outputs": [],
"source": [
"\n",
"import pandas as pd\n",
"\n",
"\n",
"\n",
"# Load the Titanic dataset\n",
"\n",
"url = \"https://raw.githubusercontent.com/moscolitos/titanic-dataset/main/Titanic-Dataset.csv\"\n",
"\n",
"titanic_data = pd.read_csv(url)\n",
"\n",
"\n",
"\n",
"# Display the first few rows of the dataset\n",
"\n",
"print(titanic_data.head())"
]
},
{
"cell_type": "markdown",
"id": "9089f70e",
"metadata": {},
"source": [
"**Understanding the DataFrame Structure**\n",
"\n",
"\n",
"\n",
"Before melting the DataFrame, it's essential to understand its structure and identify the columns we want to melt. In the Titanic dataset, we may want to melt columns related to passenger demographics, such as 'Sex', 'Age', and 'Pclass', to create a long-format DataFrame.\n",
"\n",
"\n",
"\n",
"**Melting the DataFrame**\n",
"\n",
"\n",
"\n",
"To melt a DataFrame, we use the `melt()` function in pandas. This function unpivots the DataFrame from wide to long format."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e55b9840",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Melt the DataFrame into a long format\n",
"\n",
"melted_df = pd.melt(titanic_data, id_vars=['PassengerId'], value_vars=['Sex', 'Age', 'Pclass'], var_name='Attribute', value_name='Value')\n",
"\n",
"\n",
"\n",
"print(\"Melted DataFrame:\")\n",
"\n",
"print(melted_df.head())"
]
},
{
"cell_type": "markdown",
"id": "27eebe06",
"metadata": {},
"source": [
"In this code snippet:\n",
"\n",
"- We specify the DataFrame we want to melt (`titanic_data`).\n",
"\n",
"- The `id_vars` parameter specifies the columns to keep as identifier variables (unchanged), in this case, 'PassengerId'.\n",
"\n",
"- The `value_vars` parameter specifies the columns to melt, in this case, 'Sex', 'Age', and 'Pclass'.\n",
"\n",
"- The `var_name` parameter specifies the name of the variable column that will store the original column names ('Attribute' in this case).\n",
"\n",
"- The `value_name` parameter specifies the name of the value column that will store the values corresponding to the original columns ('Value' in this case).\n",
"\n",
"\n",
"\n",
"**Conclusion**\n",
"\n",
"\n",
"\n",
"In this tutorial, we explored how to melt a DataFrame into a long format using pandas. We loaded the Titanic dataset and demonstrated the process of melting, which involves transforming a DataFrame from wide to long format. Melting data is a useful technique for reshaping data to facilitate analysis and visualization, particularly when dealing with multivariate datasets."
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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