Extract csv file specific columns to list in Python

To extract specific columns from a CSV file into a list in Python, we can use Pandas read_csv() method with the usecols parameter. This allows us to select only the columns we need, making our data processing more efficient.

Steps to Extract Specific Columns

• Create a list of column names that need to be extracted

• Use read_csv() method with usecols parameter to extract specific columns

• Convert the extracted columns to lists if needed

• Process or visualize the extracted data

Basic Column Extraction

Here's how to extract specific columns from a CSV file ?

import pandas as pd

# Sample data creation for demonstration
data = {
    'Name': ['Arun', 'Shyam', 'Govind', 'Javed', 'Raju'],
    'Marks': [98, 75, 54, 92, 87],
    'Age': [25, 23, 24, 26, 22],
    'City': ['Mumbai', 'Delhi', 'Bangalore', 'Chennai', 'Pune']
}
df_sample = pd.DataFrame(data)
print("Sample CSV data:")
print(df_sample)

Sample CSV data:
     Name  Marks  Age       City
0    Arun     98   25     Mumbai
1   Shyam     75   23      Delhi
2  Govind     54   24  Bangalore
3   Javed     92   26    Chennai
4    Raju     87   22       Pune

Extracting Specific Columns

Extract only the columns you need using usecols parameter ?

import pandas as pd

# Sample data
data = {
    'Name': ['Arun', 'Shyam', 'Govind', 'Javed', 'Raju'],
    'Marks': [98, 75, 54, 92, 87],
    'Age': [25, 23, 24, 26, 22],
    'City': ['Mumbai', 'Delhi', 'Bangalore', 'Chennai', 'Pune']
}
df_full = pd.DataFrame(data)

# Extract specific columns
columns_to_extract = ["Name", "Marks"]
df_selected = df_full[columns_to_extract]

print("Extracted columns:")
print(df_selected)

# Convert columns to lists
names_list = df_selected['Name'].tolist()
marks_list = df_selected['Marks'].tolist()

print("\nName column as list:", names_list)
print("Marks column as list:", marks_list)

Extracted columns:
     Name  Marks
0    Arun     98
1   Shyam     75
2  Govind     54
3   Javed     92
4    Raju     87

Name column as list: ['Arun', 'Shyam', 'Govind', 'Javed', 'Raju']
Marks column as list: [98, 75, 54, 92, 87]

Visualizing the Extracted Data

Create a simple plot using the extracted columns ?

import pandas as pd
import matplotlib.pyplot as plt

# Sample data
data = {
    'Name': ['Arun', 'Shyam', 'Govind', 'Javed', 'Raju'],
    'Marks': [98, 75, 54, 92, 87]
}
df = pd.DataFrame(data)

# Configure plot settings
plt.figure(figsize=(8, 5))
plt.plot(df['Name'], df['Marks'], marker='o', linewidth=2, markersize=8)
plt.title('Student Marks')
plt.xlabel('Student Names')
plt.ylabel('Marks')
plt.xticks(rotation=45)
plt.grid(True, alpha=0.3)
plt.tight_layout()

# Display basic info
print("Extracted data:")
print(df)
print(f"\nTotal students: {len(df)}")
print(f"Average marks: {df['Marks'].mean():.1f}")

Extracted data:
     Name  Marks
0    Arun     98
1   Shyam     75
2  Govind     54
3   Javed     92
4    Raju     87

Total students: 5
Average marks: 81.2

Multiple Column Extraction Methods

Conclusion

Use pd.read_csv() with usecols parameter for memory-efficient column extraction. Convert DataFrame columns to lists using .tolist() method when needed for further processing.