Simplifying DataFrame Manipulation: Multiple Ways to Add New Columns in Pandas

Using square brackets assignment:

• This is the simplest way to add a new column.
• You can assign a list, NumPy array, or a Series containing the data for the new column to the DataFrame using its column name in square brackets.
• The length of the data you assign should match the number of rows in the DataFrame.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Add a new column named 'C' with all values set to 0
df['C'] = 0

# Print the DataFrame
print(df)

This code will output:

   A  B  C
0  1  4  0
1  2  5  0
2  3  6  0

Using DataFrame.insert() method:

• This method allows you to insert a new column at a specific position within the DataFrame.
• It takes three arguments: the position (index) where you want to insert the column, the column name, and the data for the new column.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Insert a new column named 'D' at the first position
df.insert(0, 'D', ['x', 'y', 'z'])

# Print the DataFrame
print(df)

   D  A  B  C
0  x  1  4  0
1  y  2  5  0
2  z  3  6  0

• This method creates a new DataFrame with the added column, leaving the original DataFrame unchanged.
• It takes keyword arguments where the key is the column name and the value is the data for the new column.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Add a new column named 'E' with values based on existing columns
df = df.assign(E=df['A'] + df['B'])

# Print the DataFrame
print(df)

   A  B  C  E
0  1  4  0  5
1  2  5  0  7
2  3  6  0  9

These are just a few of the ways to add a new column to a DataFrame in pandas. The best method for you will depend on your specific needs.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Add a new column named 'C' with all values set to 0
df['C'] = 0

print(df)

# Insert a new column named 'D' at the first position
df.insert(0, 'D', ['x', 'y', 'z'])

print(df)

# Add a new column named 'E' with values based on existing columns
df = df.assign(E=df['A'] + df['B'])

print(df)

Using a dictionary:

• Then, assign the dictionary to the DataFrame.

# Add a new column named 'F' with random numbers
new_data = {'F': [np.random.rand() for _ in range(len(df))]}
df = pd.concat([df, pd.DataFrame(new_data)], axis=1)  # Concatenate DataFrames

print(df)

Using .loc for conditional assignment:

• This method allows you to add a new column with values based on specific conditions.

# Add a new column named 'G' with a value of 1 if 'A' is greater than 2, otherwise 0
df['G'] = np.where(df['A'] > 2, 1, 0)

print(df)

These examples demonstrate different ways to add new columns to a DataFrame in pandas. Choose the method that best suits your data manipulation needs.

Using list comprehension with square brackets assignment:

• This approach combines list comprehension with square bracket assignment for a concise way to create a new column based on existing ones.

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Add a new column 'C' with squares of 'A'
df['C'] = [x**2 for x in df['A']]

print(df)

Using apply method:

• The apply method allows you to apply a function to each row or column of the DataFrame.
• Here, you can use it to create a new column based on a function applied to existing columns.

def calculate_ratio(row):
  return row['A'] / row['B']

# Add a new column 'D' with the ratio of 'A' and 'B'
df['D'] = df.apply(calculate_ratio, axis=1)

print(df)

Concatenation with a Series:

• Create a Series containing the data for the new column.
• Concatenate the DataFrame with the Series along axis=1 (columns) to add the new column.

# Create a Series with values for the new column 'E'
new_column = pd.Series(['new_value1', 'new_value2', 'new_value3'])

# Add the Series as a new column 'E'
df = pd.concat([df, new_column], axis=1)

print(df)

These methods offer alternative ways to achieve column addition in pandas. Choose the method that best suits your data manipulation style and readability preferences.