Pandas: Working with Data

Editing and Retrieving Data

Navigating the vast landscape of data manipulation in Python, one soon realizes the power of the Pandas library. Specifically, the ability to efficiently edit and retrieve data from DataFrames is a game-changer. In this tutorial, we will delve deep into these aspects, ensuring you gain mastery over your datasets.

Pandas offers a plethora of methods to modify and access data within DataFrames. This guide focuses on the most commonly used techniques, making data manipulation a breeze.

Setting Up

Before diving in, let's create a sample DataFrame:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, 30, 35, 40],
    'City': ['New York', 'San Francisco', 'Los Angeles', 'Chicago']
}

df = pd.DataFrame(data)
print(df)

Retrieving Data

Selecting Specific Columns

names = df['Name']
print(names)

Selecting Multiple Columns

subset = df[['Name', 'City']]
print(subset)

Filtering Rows Based on Conditions

Retrieve rows where age is greater than 30:

filtered_data = df[df['Age'] > 30]
print(filtered_data)

Editing Data

Modifying a Specific Value

Change Bob's age to 31:

df.loc[df['Name'] == 'Bob', 'Age'] = 31
print(df)

Renaming Columns

df.rename(columns={'Name': 'Full Name'}, inplace=True)
print(df)

Adding New Rows

Add a new entry:

new_entry = {'Full Name': 'Eva', 'Age': 28, 'City': 'Miami'}
df = df.append(new_entry, ignore_index=True)
print(df)

Deleting Rows

Remove the entry for David:

df = df[df['Full Name'] != 'David']
print(df)

Advanced Retrieval: Using query

Pandas provides a query method, which allows for more readable and compact data retrieval:

youngsters = df.query("`Age` < 30")
print(youngsters)

Conclusion

With the ability to seamlessly edit and retrieve data, Pandas solidifies its place as a cornerstone in Python data manipulation. While this guide provides a solid foundation, the journey doesn't stop here. Dive deeper into the official Pandas documentation to uncover even more functionalities.

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