10 Commonly Used Pandas DataFrame Methods

In this blog post, we will discuss 10 commonly used Pandas DataFrame methods, along with examples of how to use them.

1. fillna()

The fillna() method fills missing values in a DataFrame with a specified value or method.

# Fill missing values in the 'age' column with the mean age
df['age'].fillna(df['age'].mean(), inplace=True)

Output:

   name    age
0  Alice   20.0
1  Bob     30.0
2  Charlie 40.0

2. dropna()

The dropna() method removes rows or columns with missing values from a DataFrame.

# Remove rows with missing values in the 'name' column
df.dropna(subset=['name'], inplace=True)

Output:

   name    age
0  Alice   20.0
1  Bob     30.0
2  Charlie 40.0

3. unique()

The unique() method returns the unique values in a DataFrame column.

# Get the unique values in the 'city' column
unique_cities = df['city'].unique()
print(unique_cities)

Output:
['New York', 'Boston', 'Chicago']

4. value_counts()

The value_counts() method counts the number of occurrences of each unique value in a DataFrame column.

# Count the number of occurrences of each unique value in the 'city' column
city_counts = df['city'].value_counts()

Output:

New York    1
Boston      1
Chicago     1

5. replace()

The replace() method replaces values in a DataFrame column with a specified value or method.

# Replace all occurrences of 'New York' with 'NYC' in the 'city' column
df['city'].replace('New York', 'NYC', inplace=True)

Output:

   name    age   city
0  Alice   20.0  NYC
1  Bob     30.0  Boston
2  Charlie 40.0  Chicago

6. astype()

The astype() method converts the data type of a DataFrame column to a specified type.

# Convert the 'age' column to a float data type
df['age'] = df['age'].astype(float)

Output:

   name     age
0  Alice    20.0
1  Bob      30.0
2  Charlie  40.0

7. to_csv()

The to_csv() method writes a DataFrame to a CSV file.

# Write the DataFrame to a CSV file
df.to_csv('output.csv', index=False)

8. to_excel()

The to_excel() method writes a DataFrame to an Excel file.

# Write the DataFrame to an Excel file
df.to_excel('output.xlsx', index=False)

9. read_csv()

The read_csv() method reads a CSV file into a DataFrame.

# Read a CSV file into a DataFrame
df = pd.read_csv('input.csv')

10. read_excel()

The read_excel() method reads an Excel file into a DataFrame.

# Read an Excel file into a DataFrame
df = pd.read_excel('input.xlsx')

Conclusion

These are just 10 of the most commonly used Pandas DataFrame methods. By understanding how to use these methods, you can easily perform a variety of data manipulation and analysis tasks in Python.