Pandas Guide

Here are some essential functions and methods in Pandas for working with data in Python. For a full reference, refer to the official Pandas documentation: Pandas Documentation.

Before you begin, make sure to import Pandas at the start of your script:

import pandas as pd

Creating DataFrames and Series

• pd.DataFrame(data, columns=None, index=None):
  • Description: Creates a DataFrame from a dictionary, list, or other data structure.
  • Parameters:
    • data: The input data (e.g., dictionary, 2D list, or NumPy array).
    • columns (optional): List of column names.
    • index (optional): List of row labels.
  • Example:

    data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
    df = pd.DataFrame(data)
    print(df)
    # Outputs:
    #     Name  Age
    # 0  Alice   25
    # 1    Bob   30
    

• pd.Series(data, index=None):
  • Description: Creates a Series, which is a one-dimensional labeled array.
  • Parameters:
    • data: Input data (e.g., list, dictionary, or scalar value).
    • index (optional): List of labels for the Series.
  • Example:

    series = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
    print(series)
    # Outputs:
    # a    1
    # b    2
    # c    3
    

Basic Operations

• df.head(n):
  • Description: Returns the first n rows of the DataFrame (default is 5).
  • Example:

    df = pd.DataFrame({'A': range(10)})
    print(df.head(3))
    # Outputs:
    #    A
    # 0  0
    # 1  1
    # 2  2
    

• df.tail(n):
  • Description: Returns the last n rows of the DataFrame (default is 5).
  • Example:

    print(df.tail(3))
    # Outputs:
    #    A
    # 7  7
    # 8  8
    # 9  9
    

• df.describe():
  • Description: Provides summary statistics of numeric columns.
  • Example:

    df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
    print(df.describe())
    # Outputs:
    #          A    B
    # count  3.0  3.0
    # mean   2.0  5.0
    # std    1.0  1.0
    # min    1.0  4.0
    # 25%    1.5  4.5
    # 50%    2.0  5.0
    # 75%    2.5  5.5
    # max    3.0  6.0
    

Data Selection and Manipulation

• df['column'] or df[column]:
  • Description: Selects a column as a Series.
  • Example:

    print(df['A'])
    # Outputs:
    # 0    1
    # 1    2
    # 2    3
    

• df.loc[row_labels, col_labels]:
  • Description: Accesses rows and columns by labels.
  • Example:

    df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}, index=['x', 'y'])
    print(df.loc['x', 'A'])
    # Outputs: 1
    

• df.iloc[row_index, col_index]:
  • Description: Accesses rows and columns by integer indices.
  • Example:

    print(df.iloc[0, 1])
    # Outputs: 3
    

• df.drop(labels, axis):
  • Description: Removes rows or columns by labels.
  • Parameters:
    • labels: Name or index of the row(s) or column(s) to drop.
    • axis: 0 for rows, 1 for columns.
  • Example:

    df = df.drop('B', axis=1)
    print(df)
    # Outputs:
    #    A
    # x  1
    # y  2
    

Grouping and Aggregations

• df.groupby(by):
  • Description: Groups the data by the specified column(s) for aggregation.
  • Parameters:
    • by: Column(s) to group by.
  • Example:

    df = pd.DataFrame({'Category': ['A', 'A', 'B'], 'Value': [10, 20, 30]})
    print(df.groupby('Category').sum())
    # Outputs:
    #           Value
    # Category       
    # A            30
    # B            30
    

For more advanced features, check out the official Pandas Documentation.