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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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Exporting Pandas DataFrame to PostgreSQL table

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

import pandas as pd
df = pd.DataFrame({'A':[3,4], 'B':['a','b']})
df
A B
0 3 a
1 4 b

To connect with the PostgreSQL database, we must use the create_engine(~) method of the sqlalchemy library:

from sqlalchemy import create_engine
engine = create_engine('postgresql://postgres@localhost:5432/test_db')

Here:

  • we are connecting to a PostgreSQL database called test_db, which is hosted on our local machine (localhost) on the default port 5432.

  • The user that we log in as is the default superuser postgres.

WARNING

If you are using a Mac and run into problems when connecting with the database, please consult our troubleshooting sectionlink here.

NOTE

To connect with your PostgreSQL server with a password or to connect with a remote server, please consult the guide here.

To save our DataFrame as a table called my_table in our PostgreSQL database, use the to_sql(~) method of the DataFrame:

df.to_sql('my_table', con=engine)

If a table with the name 'my_table' already exists, the above will throw an error. To replace the existing my_table instead:

df.to_sql('my_table', con=engine, if_exists='replace')

To confirm that our table has been created:

SELECT * FROM my_table;
index | A | B
-------+---+---
0 | 3 | a
1 | 4 | b
(2 rows)
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Published by Isshin Inada
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