Python: Pandas Merge With Examples

One of the most common operations that one has to perform while working with pandas is merging of two or more DataFrames. This can be achieved using the merge() function.

We can perform the merge on DataFrames based on either the column or index. Let's take a look at each of them with examples.

Example 1: Pandas: Merge Two DataFrames by Column Names

import pandas as pd

dataframe_open = pd.DataFrame({'TimeSeries': ["2023-01", "2023-02", "2023-03"], 'Open': [14, 22, 45]})
dataframe_high = pd.DataFrame({'TimeSeries': ["2023-01", "2023-02", "2023-03"], 'High': [16, 22, 46]})

# Merge based on column name TimeSeries
merged_timeseries_df = pd.merge(dataframe_open, dataframe_high, on='TimeSeries')

print(merged_timeseries_df)

Output:

 TimeSeries  Open  High
0    2023-01    14    16
1    2023-02    22    22
2    2023-03    45    46

Example 2: Pandas: Merge Multiple DataFrames by Column Names

import pandas as pd

dataframe_open = pd.DataFrame({'TimeSeries': ["2023-01", "2023-02", "2023-03"], 'Open': [14, 22, 45]})
dataframe_high = pd.DataFrame({'TimeSeries': ["2023-01", "2023-02", "2023-03"], 'High': [16, 22, 46]})
data_frame_low = pd.DataFrame({'TimeSeries': ["2023-01", "2023-02", "2023-03"], 'Low': [12, 20, 41]})
data_frame_close = pd.DataFrame({'TimeSeries': ["2023-01", "2023-02", "2023-03"], 'Close': [15, 21, 42]})

# Merge multiple df based on column name TimeSeries
merged_timeseries_df = pd.merge(dataframe_open, dataframe_high, on='TimeSeries')
merged_timeseries_df = pd.merge(merged_timeseries_df, data_frame_low, on='TimeSeries')
merged_timeseries_df = pd.merge(merged_timeseries_df, data_frame_close, on='TimeSeries')

print(merged_timeseries_df)

Output:

 TimeSeries  Open  High  Low  Close
0    2023-01    14    16   12     15
1    2023-02    22    22   20     21
2    2023-03    45    46   41     42

Example 3: Pandas: Merge DataFrames based on Index

import pandas as pd

dataframe_open = pd.DataFrame({'Open': [14, 22, 45]}, index=["2023-01", "2023-02", "2023-03"])
dataframe_high = pd.DataFrame({'High': [16, 22, 46]}, index=["2023-01", "2023-02", "2023-03"])
dataframe_low = pd.DataFrame({'Low': [12, 20, 41]}, index=["2023-01", "2023-02", "2023-03"])
dataframe_close = pd.DataFrame({'Close': [15, 21, 42]}, index=["2023-01", "2023-02", "2023-03"])

# Merge the DataFrames based on index
merged_df = pd.merge(dataframe_open, dataframe_high, left_index=True, right_index=True)
merged_df = pd.merge(merged_df, dataframe_low, left_index=True, right_index=True)
merged_df = pd.merge(merged_df, dataframe_close, left_index=True, right_index=True)

print(merged_df)

Output:

        Open  High  Low  Close
2023-01    14    16   12     15
2023-02    22    22   20     21
2023-03    45    46   41     42

Human-Written Content: This article was demonstrated by a human, not AI.

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https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html