Day 13: Pandas for Data Manipulation

Topics to Cover:

• Introduction to Pandas
• DataFrames and Basic Operations

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Introduction to Pandas

Pandas is a powerful data manipulation and analysis library for Python. It provides data structures and functions needed to work with structured data seamlessly.

Installing Pandas:
If you don’t have Pandas installed, you can install it using pip:

pip install pandas

Pandas DataFrames

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It is similar to a spreadsheet or SQL table.

Creating a DataFrame:

import pandas as pd

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

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

Basic Operations with DataFrames

Pandas allows you to perform a wide range of operations on DataFrames.

Loading a Dataset:

# Loading a dataset from a CSV file
df = pd.read_csv('data.csv')

Filtering Data:

# Filtering rows based on a condition
filtered_df = df[df['Age'] > 30]
print(filtered_df)

Grouping Data:

# Grouping data by a column and calculating the mean
grouped_df = df.groupby('City')['Age'].mean()
print(grouped_df)

Calculating Summary Statistics:

# Calculating summary statistics for the dataset
summary_stats = df.describe()
print(summary_stats)

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Potential Problems to Solve

Problem 1: Load a Dataset and Perform Basic Data Manipulation

Task: Load a dataset into a Pandas DataFrame and perform basic data manipulation (e.g., filtering, grouping).

Solution:

import pandas as pd

# Loading a dataset
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Age': [25, 30, 35, 40, 45],
    'City': ['New York', 'San Francisco', 'Los Angeles', 'New York', 'San Francisco']
}

df = pd.DataFrame(data)

# Filtering rows where Age is greater than 30
filtered_df = df[df['Age'] > 30]
print("Filtered DataFrame:")
print(filtered_df)

# Grouping by City and calculating the mean age
grouped_df = df.groupby('City')['Age'].mean()
print("\nGrouped DataFrame (Mean Age by City):")
print(grouped_df)

Problem 2: Calculate Summary Statistics for a Dataset

Task: Calculate summary statistics for a dataset.

Solution:

import pandas as pd

# Loading a dataset
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Age': [25, 30, 35, 40, 45],
    'City': ['New York', 'San Francisco', 'Los Angeles', 'New York', 'San Francisco']
}

df = pd.DataFrame(data)

# Calculating summary statistics
summary_stats = df.describe()
print("Summary Statistics:")
print(summary_stats)

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Conclusion

Pandas is an essential tool for data manipulation and analysis in Python. By mastering DataFrames and basic operations, you can efficiently handle and analyze data.

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Stay tuned for Day 14 of the python4ai 30-day series, where we will continue exploring advanced Python topics to enhance our programming skills!