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chevron_left Miscellaneous Cookbook
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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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Checking memory usage of DataFrame in Pandas

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schedule Jul 1, 2022
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To check the memory usage of a DataFrame in Pandas we can use the info(~) method or memory_usage(~) method. The info(~) method shows the memory usage of the whole DataFrame, while the memory_usage(~) method shows memory usage by each column of the DataFrame.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[4,5,6],"B":["K","KK","KKK"], "C": [True,False,True], "D":[4.0,5.0,6.0]}, index=[10,11,12])
df
A B C D
10 4 K True 4.0
11 5 KK False 5.0
12 6 KKK True 6.0

info method

To check the total memory usage of the DataFrame:

df.info(memory_usage="deep")
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3 entries, 10 to 12
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 A 3 non-null int64
1 B 3 non-null object
2 C 3 non-null bool
3 D 3 non-null float64
dtypes: bool(1), float64(1), int64(1), object(1)
memory usage: 252.0 bytes

We can see at the end of the output that the memory used by this DataFrame is 252 bytes. By passing memory_usage="deep" we are ensuring we calculate the actual memory usage rather than an estimate.

memory_usage method

To check the memory usage by each column of the DataFrame:

df.memory_usage(deep=True)
Index 24
A 24
B 177
C 3
D 24
dtype: int64

Note the specifying deep=True ensures we calculate the actual memory usage rather than an estimate.

By summing the memory usage of the columns and Index, we get the memory usage of the whole DataFrame:

df.memory_usage(deep=True).sum()
252

Note that this matches with the memory usage returned by the info(~) method.

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Published by Arthur Yanagisawa
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