Python Learning Series Part-3

Complete Python Topics for Data Analysis: https://t.me/sqlspecialist/548

3. Pandas:

Pandas is a powerful library for data manipulation and analysis. It provides data structures like Series and DataFrame, making it easy to handle and analyze structured data.

1. Series and DataFrame Basics:
- Series: A one-dimensional array with labels, akin to a column in a spreadsheet.

     import pandas as pd

     series_data = pd.Series([1, 3, 5, np.nan, 6, 8])
     

- DataFrame: A two-dimensional table, similar to a spreadsheet or SQL table.

     df = pd.DataFrame({
         'Name': ['Alice', 'Bob', 'Charlie'],
         'Age': [25, 30, 35],
         'City': ['New York', 'San Francisco', 'Los Angeles']
     })
     

2. Data Cleaning and Manipulation:
- Handling Missing Data: Pandas provides methods to handle missing values, like dropna() and fillna().

     df.dropna()  # Drop rows with missing values
     

- Filtering and Selection: Selecting specific rows or columns based on conditions.

     adults = df[df['Age'] > 25]
     

- Adding and Removing Columns:

     df['Salary'] = [50000, 60000, 75000]  # Adding a new column
     df.drop('City', axis=1, inplace=True)  # Removing a column
     

3. Grouping and Aggregation:
- GroupBy: Grouping data based on some criteria.

     grouped_data = df.groupby('City')
     

- Aggregation Functions: Computing summary statistics for each group.

     average_age = grouped_data['Age'].mean()
     

4. Pandas in Data Analysis:
- Pandas is extensively used for data preparation, cleaning, and exploratory data analysis (EDA).
- It seamlessly integrates with other libraries like NumPy and Matplotlib.

Here you can access Free Pandas Cheatsheet

Share with credits: https://t.me/sqlspecialist

Hope it helps :)