Counting Mulitple Values in a Python pandas Dataframe Column

Question

I'm trying to count unique values in a pandas dataframe column that contains multiple values separated by a string. I could do this using value_counts() if this were a series, but how would I do this in a dataframe? It seems like a dataframe should be easier.

Data:

                      ID       Tags
 Created at 
 2016-03-10 09:46:00  3074     tag_a
 2016-04-13 11:50:00  3524     tag_a tag_b 
 2016-05-18 15:22:00  3913     tag_a tag_b tag_c

Code:

%matplotlib inline
import pandas as pd

# read csv into the data dataframe
allData = r'myData.csv'

tickets_df = pd.read_csv((allData),usecols=['Id','Created at','Tags'],parse_dates=['Created at'], index_col=['Created at'])
tickets_df.fillna(0,inplace=True)
tickets_df['2016':'2016']

# this would work with a series:

tickets_df[tickets_df['Tags'].str.split().apply(lambda x: pd.Series(x).value_counts()).sum()]

Error:

KeyError: '[   3.    2.    3.    5.    2.  102.    9.    5.    1.    4.    1.  161.\n    4.    4.    1.    6.    4.   34.    1.    1.    1.    6.    2.    5.\n    1.    1.    1.    1.   11.    2.    1.    1.    3.    1.    1.    1.\n    1.    1.    1.    1.    2.    1.    1.    2.    2.    6.    1.    4.\n    2.    1.    1.    2.    1.    1.    1.    3.    2.    1.    4.   35.\n   11.    2.    1.   13.    3.    8.   63.   87.    2.    2.    1.    1.\n    1.    1.    1.    1.  150.    1.   24.    3.    7.    5.    1.    1.\n    3.    4.    2.    6.    1.    2.    3.    5.    2.    5.   15.    1.\n   42.    1.   14.    1.    1.    1.    6.   13.   13.    9.    2.   11.\n    3.    1.    1.] not in index'

Desired Output:

tag_a  3
tag_b  2 
tag_c  1

root · Accepted Answer · 2016-09-27 20:48:00Z

3

Use str.split with expand=True to separate each string into different columns, then use stack followed by value_counts:

df['Tags'].str.split(expand=True).stack().value_counts()

The resulting output:

tag_a    3
tag_b    2
tag_c    1

answered Sep 27, 2016 at 20:48

root

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1 Comment

FunnyChef Over a year ago

So much easier than a Series! Thank you root.

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