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
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Example #1: Use get_dtype_counts() function to find the counts of datatype of a pandas dataframe object.
# 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.
# 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.
# 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()
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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.
# Find out the types of all columns in the dataframe df.info() |
Output :