## pandas-percentage count of categorical variable

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I have a pandas df like

`df_test = pd.DataFrame({'A': 'a a a b b'.split(), 'B': ['Y','N','Y','Y','N']})`

and my desired output to be
`df_test2 = pd.DataFrame({'A': 'a b'.split(), 'B': [2/3,1/2]})`

How would you do a groupby().apply by column A to get the percentage of 'Y' in column B?

I have been searching groupby.apply() but nothing have worked so far Thank you !

One approach could be

In [10]: df_test.groupby('A').B.apply(lambda x: (x == 'Y').mean()) Out[10]: A a 0.666667 b 0.500000

or, if you don't mind changing `df_test`

in the process,

In [15]: df_test['C'] = df_test.B == 'Y' In [17]: df_test.groupby('A').C.mean() Out[17]: A a 0.666667 b 0.500000 Name: C, dtype: float64

**Pandas Series: value_counts() function,** () function is used to get a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Categorical data and Python are a data scientist’s friends. The Iris dataset is made of four metric variables and a qualitative target outcome. Just as you use means and variance as descriptive measures for metric variables, so do frequencies strictly relate to qualitative ones.

Use `GroupBy.mean`

with boolean mask, where `True`

s are processes like `1`

, no new column is necessary, because also is pass `Series`

`df_test["A"]`

to `groupby`

:

*Notice:*

*Instead == is used eq for cleaner syntax.*

df = df_test["B"].eq('Y').groupby(df_test["A"]).mean().reset_index() print (df) A B 0 a 0.666667 1 b 0.500000

**Python,** How do I count values in a column in pandas? First level index is the variable name (e.g. 'grade') Second level index is the levels within the variable (e.g. 'A', 'B', 'C') One column contains 'n', a count of the number of times the level appears; A second column contains 'proportion', the proportion represented by this level. For example:

personal favorite way:

df.column_name.value_counts() / len(df)

Gives a series with the column's values as the index and the proportions as the values.

**Pandas Tutorial 2: Aggregation and Grouping,** pandas-percentage count of categorical variable. groupby pandas percentage of total pandas percentage plot pandas calculate percentage of each row pandas Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Learn more Count number of occurrences of categorical variables in data frame (R) [duplicate]

**pandas-percentage count of categorical variable,** This is the simplest way to get the count, percenrage ( also from 0 to 100 ) at once with pandas. Let have this data: Video · Notebook. food, Portion Often while working with pandas dataframe you might have a column with categorical variables, string/characters, and you want to find the frequency counts of each unique elements present in the column. Pandas’ value_counts() easily let you get the frequency counts. Let us get started with an example from a real world data set. Load gapminder […]

**Pandas count and percentage by value for a column,** Return a Series containing counts of unique values. Bins can be useful for going from a continuous variable to a categorical variable; instead of counting Generally, the pandas data type of categorical columns is similar to simply strings of text or numerical values. However, with using ordinal categorical data types, there's a few small differences that would affect my typical workflow. Those differences in pandas are sorting as well as calculuating the minimum and maximum values in a column.

**pandas.Series.value_counts,** Percentage of a column in pandas python is carried out using sum() function in. Let's see how to Get the percentage of a column in pandas dataframe example. B = countcats(A) returns the number of elements in each category of the categorical array, A. If A is a vector, then countcats returns the number of elements in each category. If A is a matrix, then countcats treats the columns of A as vectors and returns the category counts for each column of A .