Data Manipulation with Pandas

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

To install Pandas, run the following command:

pip install pandas

Pandas mainly uses two data structures:

• Series: A one-dimensional labeled array that can hold any data type.
• DataFrame: A two-dimensional labeled data structure with columns that can hold different types.

4.1 Loading Data

Use the following code to load a CSV file into a DataFrame:

import pandas as pd
df = pd.read_csv('data.csv')

4.2 Data Inspection

To inspect the data, you can use:

• df.head() - Displays the first five rows.
• df.info() - Provides a concise summary of the DataFrame.

4.3 Data Selection

To select specific columns, use:

selected_columns = df[['column1', 'column2']]

4.4 Filtering Data

Filter rows based on conditions:

filtered_data = df[df['column1'] > 50]

4.5 Grouping Data

Group data by a specific column:

grouped_data = df.groupby('column_name').mean()

4.6 Merging DataFrames

To merge two DataFrames:

merged_df = pd.merge(df1, df2, on='key_column')

4.7 Handling Missing Values

To handle missing values, you can:

df.fillna(0, inplace=True)

or

df.dropna(inplace=True)

4.8 Data Transformation

Apply functions to columns:

df['new_column'] = df['existing_column'].apply(lambda x: x * 2)

• Always inspect data after loading to understand its structure.
• Use vectorized operations instead of loops for better performance.
• Document your code for better readability and maintainability.
• Use chaining methods for clean and efficient code.