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Pandas Append

Pandas Append

Pandas is a powerful data manipulation library in Python that offers a wide range of functionalities to manipulate and analyze data. One such functionality is the Pandas append method, which allows users to concatenate rows of one DataFrame to another, creating a new DataFrame in the process. This article will explore the Pandas append method in detail, providing comprehensive insights and numerous examples to demonstrate its usage.

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Introduction to Pandas append

The Pandas append method in Pandas is used to append rows of another DataFrame to the end of the given DataFrame, returning a new DataFrame object. This method does not change the original DataFrame but returns a new DataFrame that includes the appended rows.

Syntax of Pandas append

DataFrame._append(other, ignore_index=False, verify_integrity=False, sort=False)
  • other: DataFrame or Series/dict-like object, or list of these, to append.
  • ignore_index: If True, the index labels are not used in the resulting DataFrame. Instead, it will be labeled as 0, 1, …, n-1.
  • verify_integrity: If True, ensure that appending does not create duplicate indices.
  • sort: Sort columns if the columns of other are not in the same order.

Basic Example of Pandas append

Here is a simple example to demonstrate the basic usage of the append method.

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({
    'A': ['A0', 'A1', 'A2'],
    'B': ['B0', 'B1', 'B2']
})

df2 = pd.DataFrame({
    'A': ['A3', 'A4', 'A5'],
    'B': ['B3', 'B4', 'B5']
})

result = df1._append(df2, ignore_index=True)
print(result)

Output:

Pandas Append

Detailed Examples of Using Pandas append

Example 1: Appending Multiple DataFrames

You can append multiple DataFrames in a single operation by chaining the Pandas append method.

import pandas as pd

df1 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
})

df2 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [30, 40]
})

df3 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [50, 60]
})

result = df1._append([df2, df3], ignore_index=True)
print(result)

Output:

Pandas Append

Example 2: Appending with Different Columns

When appending DataFrames that do not have the same columns, Pandas will automatically fill in missing values with NaN.

import pandas as pd

df1 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
})

df2 = pd.DataFrame({
    'Page': ['Home', 'About'],
    'User': [100, 200]
})

result = df1._append(df2, ignore_index=True, sort=False)
print(result)

Output:

Pandas Append

Example 3: Using verify_integrity Option

The verify_integrity option can be used to ensure that the indices in the resulting DataFrame do not have duplicates.

import pandas as pd

df1 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
}, index=[0, 1])

df2 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [30, 40]
}, index=[1, 2])

try:
    result = df1._append(df2, verify_integrity=True)
    print(result)
except ValueError as e:
    print(e)

Output:

Pandas Append

Example 4: Appending a Series as a Row

You can append a Series as a row to a DataFrame. If the Series does not have a name, it will be appended as a row in the DataFrame without an index label.

import pandas as pd

df = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
})

series = pd.Series(['pandasdataframe.com', 30], index=['Site', 'Value'])

result = df._append(series, ignore_index=True)
print(result)

Output:

Pandas Append

Example 5: Appending DataFrames with MultiIndex

Appending works with MultiIndex DataFrames as well.

import pandas as pd

df1 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
}).set_index(['Site', 'Value'])

df2 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [30, 40]
}).set_index(['Site', 'Value'])

result = df1._append(df2)
print(result)

Output:

Pandas Append

Example 6: Appending with sort Option

The sort option allows you to sort the columns after appending if they are not aligned.

import pandas as pd

df1 = pd.DataFrame({
    'A': ['pandasdataframe.com', 'pandasdataframe.com'],
    'B': [10, 20]
})

df2 = pd.DataFrame({
    'B': [30, 40],
    'C': ['pandasdataframe.com', 'pandasdataframe.com']
})

result = df1._append(df2, sort=True)
print(result)

Output:

Pandas Append

Example 7: Appending Empty DataFrame

Appending an empty DataFrame will simply return the original DataFrame.

import pandas as pd

df1 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
})

df2 = pd.DataFrame()

result = df1._append(df2)
print(result)

Output:

Pandas Append

Example 8: Appending DataFrame to Itself

You can append a DataFrame to itself to duplicate its rows.

import pandas as pd

df = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
})

result = df._append(df, ignore_index=True)
print(result)

Output:

Pandas Append

Example 9: Appending with Different Index Types

When appending DataFrames with different index types, the resulting DataFrame will have a mixed type index unless ignore_index=True is used.

import pandas as pd

df1 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
}, index=[0, 1])

df2 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [30, 40]
}, index=['a', 'b'])

result = df1._append(df2)
print(result)

Output:

Pandas Append

Example 10: Appending with No Common Columns

When there are no common columns between the DataFrames, the resulting DataFrame will have all columns from both DataFrames, filled with NaN where necessary.

import pandas as pd

df1 = pd.DataFrame({
    'Site': ['pandasdataframe.com', 'pandasdataframe.com'],
    'Value': [10, 20]
})

df2 = pd.DataFrame({
    'Page': ['Home', 'About'],
    'User': [100, 200]
})

result = df1._append(df2, ignore_index=True)
print(result)

Output:

Pandas Append

Pandas append conclusion

The Pandas append method in Pandas is a versatile tool for combining DataFrames by adding rows. It offers options to handle indices, sort columns, and manage data integrity. By understanding and utilizing these features effectively, you can streamline your data manipulation tasks in Python. The examples provided in this article should help you get started with using Pandas append in your own projects.

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