Why Does Column Order Change When Appending Pandas DataFrames

When appending Pandas DataFrames, we may notice that the column order has changed. This behavior can be confusing, especially if we want the columns to maintain a specific order. This article explains why this happens and how to ensure the column order remains consistent.

Understanding Column Order in Pandas

Managing Pandas DataFrames are inherently column-indexed structures, which means that the order of columns is important for both data manipulation and presentation. Each column in a DataFrame is indexed by its label, and the order of the columns typically follows the order of their definition.

However, this column order can sometimes change, especially when appending DataFrames that may not have identical structures or contain different column names. Understanding how column alignment works is essential to managing and maintaining column order.

Reasons Why Column Order Changes

The column order may change when appending Pandas DataFrames because of the following reasons: If the columns are not in the same order, the column order in the resulting dataframe may change.

To preserve the column order, we can do the following:

• Reorder Columns After Appending: If column order changes after appending, we can explicitly reorder the columns based on a reference DataFrame.
• Specify Columns When Appending: We can append DataFrames by specifying the order of columns manually.

Preserving Column Order when Appending

If we want to control the column order when appending DataFrames, we can use the following techniques:

1. Ensure Consistent Column Order Before Appending

We can reorder the columns in the DataFrames before appending, ensuring they have the same column order. By reordering df2 to have columns 'A', 'B', 'C', the final appended DataFrame retains this order.

import pandas as pd

# First DataFrame with columns 'A', 'B', 'C'
df1 = pd.DataFrame({
    'A': [1, 2],
    'B': [3, 4],
    'C': [5, 6]
})

# Second DataFrame with columns 'C', 'B', 'A'
df2 = pd.DataFrame({
    'C': [7, 8],
    'B': [9, 10],
    'A': [11, 12]
})

# Reorder df2 to match the column order of df1
df2 = df2[['A', 'B', 'C']]

# Append with consistent column order
df_appended = pd.concat([df1, df2], ignore_index=True)
print(df_appended)

Output:

     A   B  C
0   1   3  5
1   2   4  6
2  11   9  7
3  12  10  8

2. Use concat() with reindex()

We can also use pd.concat() along with reindex() to explicitly define the column order after appending. This ensures that the final DataFrame has the specified column order.

import pandas as pd

# First DataFrame with columns 'A', 'B', 'C'
df1 = pd.DataFrame({
    'A': [1, 2],
    'B': [3, 4],
    'C': [5, 6]
})

# Second DataFrame with columns 'C', 'B', 'A'
df2 = pd.DataFrame({
    'C': [7, 8],
    'B': [9, 10],
    'A': [11, 12]
})

# Concatenate df1 and df2
df_appended = pd.concat([df1, df2], ignore_index=True)

# Reindex to maintain the desired column order
df_appended = df_appended.reindex(columns=['A', 'B', 'C'])
print(df_appended)

Output:

    A   B  C
0   1   3  5
1   2   4  6
2  11   9  7
3  12  10  8

Conclusion

The column order changes when appending DataFrames because Pandas aligns columns by their names rather than their positions. This behavior is particularly evident when the DataFrames have different column orders or when some columns are missing in one of the DataFrames. To maintain a consistent column order, we can reorder the columns before appending or reindex the columns after appending.