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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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Highlighting a particular cell of a DataFrame in Pandas

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Miscellaneous Cookbook
schedule Jul 1, 2022
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To highlight a particular cell of a DataFrame, use the DataFrame's style.apply(~) method.

Example

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,4],"B":[5,6]}, index=["a","b"])
df
A B
a 3 5
b 4 6

Solution

To highlight the cell bA:

import numpy as np

# Our custom styling method - this is fired once for each column (Series) of the DataFrame
def highlight_cell(col, col_label, row_label):
   # check if col is a column we want to highlight
if col.name == col_label:
       # a boolean mask where True represents a row we want to highlight
mask = (col.index == row_label)
        # return an array of string styles (e.g. ["", "background-color: yellow"])
return ["background-color: yellow" if val_bool else "" for val_bool in mask]
else:
        # return an array of empty strings that has the same size as col (e.g. ["",""])
return np.full_like(col, "", dtype="str")

df.style.apply(highlight_cell, col_label="A", row_label="b")

The output is as follows:

robocat
Published by Isshin Inada
Edited by 0 others
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