Pandas Append Row

Pandas Append Row

Pandas is a powerful Python library used for data manipulation and analysis. One of the common tasks when working with data is appending rows to an existing DataFrame. This article will explore various methods to append rows to a DataFrame using Pandas, providing detailed examples for each method.

Introduction to DataFrame

A DataFrame is a two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Before diving into appending rows, let’s first understand how to create a DataFrame.

Example 1: Creating a DataFrame

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)
print(df)

Output:

Appending Rows to a DataFrame

Appending rows to a DataFrame is a common operation. This can be done in several ways including using loc, append, and concat methods.

Example 2: Appending a Row Using loc

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)

df.loc[len(df)] = ['Charlie', 'pandasdataframe.com']
print(df)

Output:

Example 3: Appending a Row Using append Method

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)

new_row = pd.Series(['David', 'pandasdataframe.com'], index=df.columns)
df = df._append(new_row, ignore_index=True)
print(df)

Output:

Example 4: Appending Multiple Rows Using append Method

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)

new_rows = pd.DataFrame([
    ['Eve', 'pandasdataframe.com'],
    ['Frank', 'pandasdataframe.com']
], columns=df.columns)
df = df._append(new_rows, ignore_index=True)
print(df)

Output:

Example 5: Appending Rows Using concat

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)

rows_to_add = pd.DataFrame([
    {'Name': 'Grace', 'Website': 'pandasdataframe.com'},
    {'Name': 'Heidi', 'Website': 'pandasdataframe.com'}
])
df = pd.concat([df, rows_to_add], ignore_index=True)
print(df)

Output:

Advanced Row Appending Techniques

Sometimes, you might need to append rows based on certain conditions or from different data sources.

Example 6: Conditional Appending of Rows

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)

if 'Judy' not in df['Name'].values:
    df = df._append({'Name': 'Judy', 'Website': 'pandasdataframe.com'}, ignore_index=True)
print(df)

Output:

Example 7: Appending Rows from Another DataFrame

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)

additional_data = pd.DataFrame({
    'Name': ['Ivy', 'John'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
})
df = pd.concat([df, additional_data], ignore_index=True)
print(df)

Output:

Handling Different Data Types When Appending Rows

DataFrames can contain columns of different data types. When appending rows, it’s important to ensure that the data types are consistent.

Example 8: Appending Rows with Different Column Data Types

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)

df['Age'] = pd.Series([25, 30], index=df.index)
new_row = pd.Series(['Kathy', 'pandasdataframe.com', 22], index=['Name', 'Website', 'Age'])
df = df._append(new_row, ignore_index=True)
print(df)

Output:

Performance Considerations

Appending rows to a DataFrame can be computationally expensive, especially for large DataFrames. It’s often more efficient to collect all rows to be added in a list or another DataFrame and append them all at once.

Example 9: Efficiently Appending Multiple Rows

import pandas as pd

data = {
    'Name': ['Alice', 'Bob'],
    'Website': ['pandasdataframe.com', 'pandasdataframe.com']
}
df = pd.DataFrame(data)

rows = [{'Name': 'Liam', 'Website': 'pandasdataframe.com'},
        {'Name': 'Mia', 'Website': 'pandasdataframe.com'}]
df = pd.concat([df, pd.DataFrame(rows)], ignore_index=True)
print(df)

Output:

Pandas Append Row Conclusion

Appending rows to a DataFrame is a fundamental operation in data manipulation with Pandas. This article has demonstrated various methods to append rows, each suitable for different scenarios. Whether you’re dealing with small or large datasets, understanding these techniques is crucial for efficient data analysis.

Remember, while append and loc are convenient for small DataFrames or infrequent operations, concat is typically more efficient for larger or more frequent row additions. Always consider the size and frequency of data manipulation when choosing your method to ensure optimal performance.