Tag: python

Rename Specific Columns

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Age': [25, 30]
})

# Rename the 'Name' column to 'Full Name' and 'Age' to 'Age in Years'
df.rename(columns={'Name': 'Full Name', 'Age': 'Age in Years'}, inplace=True)

print(df)

Modify All Headers at Once

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Age': [25, 30]
})

# New column names
new_columns = ['Full Name', 'Age in Years']

# Assign the new column names to the DataFrame
df.columns = new_columns

print(df)

Apply a Function to Headers

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Age': [25, 30]
})

# Make all column names uppercase
df.columns = df.columns.str.upper()

print(df)

import pandas as pd

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

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# Remove the 'Age' column
df = df.drop(columns=['Age'])

# Print the updated DataFrame
print("\nDataFrame After Removing 'Age' Column:")
print(df)

Remember to assign the result back to the DataFrame (or to a new variable) if you want to keep the change. If you just want to remove the column temporarily for a specific operation, you can use the inplace=True argument to modify the DataFrame in place:

df.drop(columns=['Age'], inplace=True)

You can remove a column from a DataFrame by its index

import pandas as pd

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

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# Index of the column to be removed
column_index = 1

# Get the name of the column at the specified index
column_name = df.columns[column_index]

# Drop the column by its name
df.drop(columns=[column_name], inplace=True)

# Print the updated DataFrame
print("\nDataFrame After Removing Column at Index 1:")
print(df)

Remove multiple columns at once from a DataFrame in Pandas

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago'],
    'Country': ['USA', 'USA', 'USA']
})

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# List of columns to be removed
columns_to_remove = ['Age', 'Country']

# Drop the specified columns
df.drop(columns=columns_to_remove, inplace=True)

# Print the updated DataFrame
print("\nDataFrame After Removing Columns:")
print(df)

If you want to remove multiple columns by their indices, you can use the following code:

# List of column indices to be removed
column_indices_to_remove = [1, 3]

# Get the names of the columns at the specified indices
columns_to_remove = df.columns[column_indices_to_remove]

# Drop the specified columns
df.drop(columns=columns_to_remove, inplace=True)

Using concat to Stack DataFrames Vertically

If the two DataFrames have the same columns and you want to stack them vertically, you can use the pd.concat method:

import pandas as pd

# Define the first DataFrame
df1 = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Age': [25, 30],
    'City': ['New York', 'Los Angeles']
})

# Define the second DataFrame
df2 = pd.DataFrame({
    'Name': ['Charlie', 'David'],
    'Age': [35, 40],
    'City': ['Chicago', 'Houston']
})

# Concatenate the two DataFrames
result = pd.concat([df1, df2])

# Print the result
print(result)

Using merge to Join DataFrames Horizontally

If you want to join two DataFrames based on a common column (for example, an ID), you can use the pd.merge method:

# Define the first DataFrame
df1 = pd.DataFrame({
    'ID': [1, 2],
    'Name': ['Alice', 'Bob']
})

# Define the second DataFrame
df2 = pd.DataFrame({
    'ID': [1, 2],
    'Age': [25, 30]
})

# Merge the two DataFrames on the 'ID' column
result = pd.merge(df1, df2, on='ID')

# Print the result
print(result)