Effortlessly Combine Dataframes in Pandas
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Combining Data in pandas With merge(), .join(), and concat()
The Series and DataFrame objects in pandas are powerful tools for exploring and analyzing data. Part of their power comes from a multifaceted approach to combining separate datasets. With pandas, you can merge, join, and concatenate your datasets, allowing you to unify and better understand your data as you analyze it.
pandas merge(): Combining Data on Common Columns or Indices
The first technique that you’ll learn is merge(). You can use merge() anytime you want functionality similar to a database’s join operations. It’s the most flexible of the three operations that you’ll learn.
How to Use merge()
The merge() function in pandas allows you to merge two DataFrame objects on common columns or indices. It takes the following general form:
merged_df = pd.merge(left_df, right_df, how=merge_type, on=key_columns)Here’s an explanation of the parameters:
left_df: The left DataFrame object that you want to merge.right_df: The right DataFrame object that you want to merge.how: The type of merge to perform. Options include ‘inner’, ‘outer’, ‘left’, and ‘right’.on: The key columns that you want to merge on.
Examples
Let’s take a look at a few examples to see merge() in action:
Example 1: Merge on a Single Column
import pandas as pd
# Define the first DataFramedf1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value1': [1, 2, 3, 4]})
# Define the second DataFramedf2 = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'value2': [5, 6, 7, 8]})
# Merge the DataFrames on the 'key' columnmerged_df = pd.merge(df1, df2, how='inner', on='key')
print(merged_df)Output:
key value1 value20 B 2 51 D 4 6In this example, we have two DataFrames (df1 and df2) with a common column called ‘key’. By using merge() with an inner join, we obtain a new DataFrame (merged_df) that only contains rows where the ‘key’ values match in both DataFrames.
Example 2: Merge on Multiple Columns
import pandas as pd
# Define the first DataFramedf1 = pd.DataFrame({'key1': ['A', 'B', 'C', 'D'], 'key2': [1, 2, 3, 4], 'value1': [1, 2, 3, 4]})
# Define the second DataFramedf2 = pd.DataFrame({'key1': ['B', 'D', 'E', 'F'], 'key2': [2, 4, 5, 6], 'value2': [5, 6, 7, 8]})
# Merge the DataFrames on the 'key1' and 'key2' columnsmerged_df = pd.merge(df1, df2, how='inner', on=['key1', 'key2'])
print(merged_df)Output:
key1 key2 value1 value20 B 2 2 51 D 4 4 6In this example, we have two DataFrames (df1 and df2) with multiple common columns (‘key1’ and ‘key2’). By specifying a list of key columns (['key1', 'key2']), we perform a merge on both columns and obtain a new DataFrame (merged_df) that only contains rows where the values in both ‘key1’ and ‘key2’ match in both DataFrames.
pandas .join(): Combining Data on a Column or Index
The second technique that you’ll learn is .join(). You can use .join() when you want to combine data on a key column or an index. It provides a more convenient syntax compared to merge() when working with DataFrames.
How to Use .join()
The .join() method in pandas allows you to join two DataFrames on a key column or an index. It takes the following general form:
joined_df = left_df.join(right_df, on=key_column/index)Here’s an explanation of the parameters:
left_df: The left DataFrame object that you want to join.right_df: The right DataFrame object that you want to join.on: The key column or index to join on.
Examples
Let’s take a look at a few examples to see .join() in action:
Example 1: Join on a Key Column
import pandas as pd
# Define the first DataFramedf1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value1': [1, 2, 3]})
# Define the second DataFramedf2 = pd.DataFrame({'value2': [4, 5, 6]}, index=['A', 'B', 'C'])
# Join the DataFrames on the 'key' columnjoined_df = df1.join(df2, on='key')
print(joined_df)Output:
key value1 value20 A 1 41 B 2 52 C 3 6In this example, we have two DataFrames (df1 and df2). By using .join() on the ‘key’ column, we obtain a new DataFrame (joined_df) that combines the data from both DataFrames based on matching values in the ‘key’ column.
Example 2: Join on an Index
import pandas as pd
# Define the first DataFramedf1 = pd.DataFrame({'value1': [1, 2, 3]}, index=['A', 'B', 'C'])
# Define the second DataFramedf2 = pd.DataFrame({'value2': [4, 5, 6]}, index=['A', 'B', 'C'])
# Join the DataFrames on the indexjoined_df = df1.join(df2)
print(joined_df)Output:
value1 value2A 1 4B 2 5C 3 6In this example, we have two DataFrames (df1 and df2) with matching indices. By using .join() without specifying a key column, we obtain a new DataFrame (joined_df) that combines the data from both DataFrames based on the matching indices.
pandas concat(): Combining Data Across Rows or Columns
The third technique that you’ll learn is concat(). You can use concat() when you want to combine DataFrames across rows or columns. It provides a convenient way to stack DataFrames vertically or horizontally.
How to Use concat()
The concat() function in pandas allows you to concatenate two or more DataFrames along a specified axis (either 0 for rows or 1 for columns). It takes the following general form:
concatenated_df = pd.concat([df1, df2, df3], axis=axis)Here’s an explanation of the parameters:
df1,df2,df3, …: The DataFrame objects that you want to concatenate.axis: The axis along which to concatenate the DataFrames. Use 0 to stack the DataFrames vertically and 1 to stack them horizontally.
Examples
Let’s take a look at a few examples to see concat() in action:
Example 1: Concatenate Along Rows
import pandas as pd
# Define the first DataFramedf1 = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
# Define the second DataFramedf2 = pd.DataFrame({'name': ['Charlie', 'Dave'], 'age': [35, 40]})
# Concatenate the DataFrames along rowsconcatenated_df = pd.concat([df1, df2], axis=0)
print(concatenated_df)Output:
name age0 Alice 251 Bob 300 Charlie 351 Dave 40In this example, we have two DataFrames (df1 and df2). By using concat() with axis=0, we obtain a new DataFrame (concatenated_df) that stacks the rows of both DataFrames vertically.
Example 2: Concatenate Along Columns
import pandas as pd
# Define the first DataFramedf1 = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [25, 30]})
# Define the second DataFramedf2 = pd.DataFrame({'city': ['New York', 'San Francisco'], 'state': ['NY', 'CA']})
# Concatenate the DataFrames along columnsconcatenated_df = pd.concat([df1, df2], axis=1)
print(concatenated_df)Output:
name age city state0 Alice 25 New York NY1 Bob 30 San Francisco CAIn this example, we have two DataFrames (df1 and df2). By using concat() with axis=1, we obtain a new DataFrame (concatenated_df) that concatenates the columns of both DataFrames horizontally.
Conclusion
In this tutorial, you learned how to combine your data in pandas using the merge(), .join(), and concat() methods. You explored their various functionalities and saw examples of how to use them in practice. By mastering these techniques, you now have a powerful set of tools to manipulate and analyze your data in pandas.
Remember, practice makes perfect, so feel free to experiment with these methods using your own datasets. Happy coding!
