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chevron_left Miscellaneous Cookbook
Adjusting number of rows that are printedAppending DataFrame to an existing CSV fileChecking differences between two indexesChecking if a DataFrame is emptyChecking if a variable is a DataFrameChecking if index is sortedChecking if value exists in IndexChecking memory usage of DataFrameChecking whether a Pandas object is a view or a copyConcatenating a list of DataFramesConverting a DataFrame to a listConverting a DataFrame to a SeriesConverting DataFrame to a list of dictionariesConverting DataFrame to list of tuplesCounting the number of negative valuesCreating a DataFrame using cartesian product of two DataFramesDisplaying DataFrames side by sideDisplaying full non-truncated DataFrame valuesDrawing frequency histogram of DataFrame columnExporting Pandas DataFrame to PostgreSQL tableHighlighting a particular cell of a DataFrameHighlighting DataFrame cell based on valueHow to solve "ValueError: If using all scalar values, you must pass an index"Importing BigQuery table as Pandas DataFramePlotting two columns of DataFramePrinting DataFrame on a single linePrinting DataFrame without indexPrinting DataFrames in tabular formatRandomly splitting DataFrame into multiple DataFrames of equal sizeReducing DataFrame memory sizeSaving a DataFrame as a CSV fileSaving DataFrame as Excel fileSaving DataFrame as feather fileSetting all values to zeroShowing all dtypes without truncationSplitting DataFrame into multiple DataFrames based on valueSplitting DataFrame into smaller equal-sized DataFramesWriting DataFrame to SQLite
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chevron_left Miscellaneous Cookbook
Adjusting number of rows that are printedAppending DataFrame to an existing CSV fileChecking differences between two indexesChecking if a DataFrame is emptyChecking if a variable is a DataFrameChecking if index is sortedChecking if value exists in IndexChecking memory usage of DataFrameChecking whether a Pandas object is a view or a copyConcatenating a list of DataFramesConverting a DataFrame to a listConverting a DataFrame to a SeriesConverting DataFrame to a list of dictionariesConverting DataFrame to list of tuplesCounting the number of negative valuesCreating a DataFrame using cartesian product of two DataFramesDisplaying DataFrames side by sideDisplaying full non-truncated DataFrame valuesDrawing frequency histogram of DataFrame columnExporting Pandas DataFrame to PostgreSQL tableHighlighting a particular cell of a DataFrameHighlighting DataFrame cell based on valueHow to solve "ValueError: If using all scalar values, you must pass an index"Importing BigQuery table as Pandas DataFramePlotting two columns of DataFramePrinting DataFrame on a single linePrinting DataFrame without indexPrinting DataFrames in tabular formatRandomly splitting DataFrame into multiple DataFrames of equal sizeReducing DataFrame memory sizeSaving a DataFrame as a CSV fileSaving DataFrame as Excel fileSaving DataFrame as feather fileSetting all values to zeroShowing all dtypes without truncationSplitting DataFrame into multiple DataFrames based on valueSplitting DataFrame into smaller equal-sized DataFramesWriting DataFrame to SQLite
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Creating a Pandas DataFrame using cartesian product of two DataFrames

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Miscellaneous Cookbook
schedule Jul 1, 2022
Last updated
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Consider the following DataFrame:

import pandas as pd
df1 = pd.DataFrame({'A':[3,4],'B':[5,6]})
df1
A B
0 3 5
1 4 6

The other DataFrame is as follows:

df2 = pd.DataFrame({'C':[7,8]})
df2
C
0 7
1 8

To create a new DataFrame using the cartesian product of two DataFrames:

df1.merge(df2, how='cross')
A B C
0 3 5 7
1 3 5 8
2 4 6 7
3 4 6 8
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Published by Isshin Inada
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