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Python | Pandas Series.get_dtype_counts ()

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Series.get_dtype_counts() Pandas Series.get_dtype_counts() returns the number of unique dtypes in this object.

Syntax: Series.get_values ​​()

Parameter: None

Returns: dtype: Series

Example # 1: Use Series.get_dtype_counts () to return the number of unique dtype in a given series object.

# import pandas as pd

import pandas as pd

 
# Series creation

sr = pd.Series ([ ’New York’ , ’ Chicago’ , ’Toronto’ , None , ’Rio ’ ])

  
# Create Index

index_ = [ ’City 1’ , ’ City 2’ , ’City 3’ , ’ City 4’ , ’City 5’

 
# set index

sr.index = index_

 
# Print series

print (sr)

Output:

We will now use Series.get_dtype_counts () to return the number of unique dytpe in this series object.

# return the number of dtypes

result = sr.get_dtype_counts ()

 
# Print result

print (result)

Output:

As we can see in the output, Series.get_dtype_counts () returned the dtype counter for the given series object. It returned an object.

Example # 2: Use Series.get_dtype_counts () to return the number of unique dtypes in a given series object.

# import pandas as pd

import pandas as pd

 
# Create a series

sr = pd.Series ([ 11 , 21 , 8 , 18 , 65 , 84 , 32 , 10 , 5 , 24 , 32 ])

 
# Create Index

index_ = pd.date_range ( ’2010-10-09’ , periods = 11 , freq = ’ M’ )

 
# set index

sr.index = index_

 
# Print series

print (sr)

Output:

We will now use Series.get_dtype_counts () to return the number of unique dytpes in a given series object.

# return the number of dtypes

result = sr.get_dtype_counts ()

 
# Print result

print (result)

Output:

As we can see in the output, Series.get_dtype_counts () returned the dtype counter in this series object. Returned int64.

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