Below is a checklist of common commands for DataFrame combination in pandas, along with examples and expected outputs:
In pandas, join, merge, and concat are three different methods used for combining or merging DataFrames. Here's an explanation of each along with examples and outputs:
concat
The concat function is used for concatenating DataFrames along a particular axis (either rows or columns).
import pandas as pd
# Example DataFrames
df1 = pd.DataFrame({'A': ['A0', 'A1'], 'B': ['B0', 'B1']})
df2 = pd.DataFrame({'A': ['A2', 'A3'], 'B': ['B2', 'B3']})
# Concatenating along rows (axis=0)
result_row = pd.concat([df1, df2])
print("Concatenate along rows:")
print(result_row)
# Concatenating along columns (axis=1)
result_col = pd.concat([df1, df2], axis=1)
print("\nConcatenate along columns:")
print(result_col)
Output:
Concatenate along rows:
A B
0 A0 B0
1 A1 B1
0 A2 B2
1 A3 B3
Concatenate along columns:
A B A B
0 A0 B0 A2 B2
1 A1 B1 A3 B3
merge
:
The merge function is used for combining DataFrames based on a common column or index.
# Example DataFrames
df1 = pd.DataFrame({'key': ['K0', 'K1'], 'value': ['V0', 'V1']})
df2 = pd.DataFrame({'key': ['K0', 'K1'], 'value': ['V2', 'V3']})
# Merging based on the 'key' column
result = pd.merge(df1, df2, on='key')
print("Merge based on 'key' column:")
print(result)
Output:
Merge based on 'key' column:
key value_x value_y
0 K0 V0 V2
1 K1 V1 V3
join
The join method is used for combining DataFrames based on their indexes.
# Example DataFrames with shared index
df1 = pd.DataFrame({'A': ['A0', 'A1'], 'B': ['B0', 'B1']}, index=['K0', 'K1'])
df2 = pd.DataFrame({'C': ['C0', 'C1'], 'D': ['D0', 'D1']}, index=['K0', 'K1'])
# Joining based on the index
result = df1.join(df2)
print("Join based on index:")
print(result)
Output:
Join based on index:
A B C D
K0 A0 B0 C0 D0
K1 A1 B1 C1 D1
Key Differences
- concat is used for simple concatenation along a specified axis.
- merge is used for combining DataFrames based on the values of specified columns.
- join is used for combining DataFrames based on their indexes .
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