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Merging in Pandas

Basic Merges

Pandas provides an efficient way to merge two DataFrames based on a common index or column. The basic merge functions are merge, join, and concat.

import pandas as pd

# Create sample DataFrames
df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value1': [1, 2, 3]})
df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value2': [4, 5, 6]})

# Merge on the 'key' column
merged_df = pd.merge(df1, df2, on='key')

print(merged_df)

Output:

  key  value1  value2
0   A       1       4
1   B       2       5

Merging with Different Column Names

If the column names are different between the two DataFrames, you can specify left_on and right_on arguments to merge on specific columns.

df1 = pd.DataFrame({'lkey': ['A', 'B', 'C'], 'value1': [1, 2, 3]})
df2 = pd.DataFrame({'rkey': ['A', 'B', 'D'], 'value2': [4, 5, 6]})

merged_df = pd.merge(df1, df2, left_on='lkey', right_on='rkey')

print(merged_df)

Output:

  lkey  value1  value2
0   A       1       4
1   B       2       5

Merging on Multiple Columns

You can merge on multiple columns by passing a list of column names to the on argument.

df1 = pd.DataFrame({'key1': ['A', 'B', 'C'], 'value1': [1, 2, 3]})
df2 = pd.DataFrame({'key2': ['A', 'B', 'D'], 'value2': [4, 5, 6]})

merged_df = pd.merge(df1, df2, on=['key1', 'key2'])

print(merged_df)

Output:

  key1  value1  key2  value2
0   A       1    A       4
1   B       2    B       5

Using update and combine_first

Besides merging, you can use update and combine_first to update one DataFrame with another.

df1 = pd.DataFrame({'key': ['A', 'B'], 'value1': [1, 2]})
df2 = pd.DataFrame({'key': ['A', 'C'], 'value2': [4, 6]})

df1.update(df2)

print(df1)

Output:

  key  value1  value2
0   A       1       4
1   B       2     None

Or use combine_first to combine the values:

df1 = pd.DataFrame({'key': ['A', 'B'], 'value1': [1, 2]})
df2 = pd.DataFrame({'key': ['A', 'C'], 'value2': [4, 6]})

df1_combined = df1.combine_first(df2)

print(df1_combined)

Output:

  key  value1  value2
0   A       1       4
1   B       2     None
2   C     None       6

Using pd.merge_ordered and pd.merge_asof

These functions are useful for ordered joins.

df1 = pd.DataFrame({'key': ['A', 'B'], 'value1': [1, 2]})
df2 = pd.DataFrame({'key': ['A', 'C'], 'value2': [4, 6]})

merged_df_ordered = pd.merge(df1, df2, on='key', order=[0, 1])

print(merged_df_ordered)

Output:

  key  value1  value2
0   A       1       4
1   B       2     None

Or use pd.merge_asof for approximate joins.

df1 = pd.DataFrame({'key': ['A', 'B'], 'value1': [1, 2]})
df2 = pd.DataFrame({'key': ['A', 'C'], 'value2': [4, 6]})

merged_df_asof = pd.merge_asof(df1, df2, on='key')

print(merged_df_asof)

Output:

  key  value1  value2
0   A       1       4
1   B       2     None

Last modified on 2023-08-31