Pandas Series.str.count()

Python Pandas Series.str.count()

Pandas str.count() method is used to count occurrence of a string or regex pattern in each string of a series. Additional flags arguments can also be passed to handle to modify some aspects of regex like case sensitivity, multi line matching etc.

Since this is a pandas string method, it’s only applicable on Series of strings and .str has to be prefixed every time before calling this method. Otherwise, it will give an error


Syntax: Series.str.count(pat, flags=0)

pat: String or regex to be searched in the strings present in series
flags: Regex flags that can be passed (A, S, L, M, I, X), default is 0 which means None. For this regex module (re) has to be imported too.

Return type: Series with count of occurrence of passed characters in each string.

To download the CSV used in code, click here.

In the following examples, the data frame used contains data of some NBA players. The image of data frame before any operations is attached below.

Example #1: Counting word occurrence
In this example, a Pandas series is made from a list and occurrence of gfg is counted using str.count() method.

# importing pandas package
import pandas as pd
# making list
list =["GeeksforGeeks", "Geeksforgeeks", "geeksforgeeks",
       "geeksforgeeks is a great platform", "for tech geeks"]
# making series
series = pd.Series(list)
# counting occurrence of geeks
count = series.str.count("geeks")
# display

As shown in the output image, the occurrence of geeks in each string was displayed and Geeks wasn’t counted due to first upper case letter.

Example #2: Using Flags

In this example, occurrence of “a” is counted in the Name column. The flag parameter is also used and re.I is passed to it, which means IGNORECASE. Hence, a and A both will be considered during count.

# importing pandas module 
import pandas as pd
# importing module for regex
import re
# reading csv file from url 
# String to be searched in start of string 
search ="a"
# count of occurrence of a and creating new column
data["count"]= data["Name"].str.count(search, re.I)
# display

As shown in the output image, it can be clearly compared by looking at the first index itself, the count for a in Avery Bradely is 2, which means both upper case and lower case was considered.

Last Updated on October 18, 2021 by admin

Leave a Reply

Your email address will not be published. Required fields are marked *

Recommended Blogs