Python | Pandas dataframe.get_dtype_counts()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.get_dtype_counts() function returns the counts of dtypes in the given object. It returns a pandas series object containing the counts of all data types present in the pandas object. It works with pandas series as well as dataframe.

Syntax: DataFrame.get_dtype_counts() Returns : value : Series : Counts of datatypes

For link to CSV file Used in Code, click here Example #1: Use get_dtype_counts() function to find the counts of datatype of a pandas dataframe object. Python3

# importing pandas as pd
import pandas as pd

# Creating the dataframe 
df = pd.read_csv("nba.csv")

# Print the dataframe
df

Now apply the get_dtype_counts() function. Find out the frequency of occurrence of each data type in the dataframe. Python3 1==

# applying get_dtype_counts() function 
df.get_dtype_counts()

Output : Notice, the output is a pandas series object containing the count of each data types in the dataframe.   Example #2: Use get_dtype_counts() function over a selected no. of columns of the data frame only. Python3

# importing pandas as pd
import pandas as pd

# Creating the dataframe 
df = pd.read_csv("nba.csv")

# Applying get_dtype_counts() function to 
# find the data type counts in modified dataframe.
df[["Salary", "Name", "Team"]].get_dtype_counts()

Notice, the output is a pandas series object containing the count of each data types in the dataframe. We can verify all these results using this the dataframe.info() function. Python3 1==

# Find out the types of all columns in the dataframe
df.info()

Output :