In this article we will discuss different ways to fetch the data type of single or multiple columns. Also see how to compare data types of columns and fetch column names based on data types.
Use Dataframe.dtypes to get Data types of columns in Dataframe
In Python’s pandas module Dataframe class provides an attribute to get the data type information of each columns i.e.
Dataframe.dtypes
It returns a series object containing data type information of each column. Let’s use this to find & check data types of columns.
Suppose we have a Dataframe i.e.
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# List of Tuples
empoyees = [('jack', 34, 'Sydney', 155),
('Riti', 31, 'Delhi', 177.5),
('Aadi', 16, 'Mumbai', 81),
('Mohit', 31, 'Delhi', 167),
('Veena', 12, 'Delhi', 144),
('Shaunak', 35, 'Mumbai', 135),
('Shaun', 35, 'Colombo', 111)
]
# Create a DataFrame object
empDfObj = pd.DataFrame(empoyees, columns=['Name', 'Age', 'City', 'Marks'])
print(empDfObj)
Contents of the dataframe are,
Name Age City Marks 0 jack 34 Sydney 155.0 1 Riti 31 Delhi 177.5 2 Aadi 16 Mumbai 81.0 3 Mohit 31 Delhi 167.0 4 Veena 12 Delhi 144.0 5 Shaunak 35 Mumbai 135.0 6 Shaun 35 Colombo 111.0
Let’s fetch the Data type of each column in Dataframe as a Series object,
# Get a Series object containing the data type objects of each column of Dataframe.
# Index of series is column name.
dataTypeSeries = empDfObj.dtypes
print('Data type of each column of Dataframe :')
print(dataTypeSeries)
Output
Data type of each column of Dataframe : Name object Age int64 City object Marks float64 dtype: object
Index of returned Series object is column name and value column of Series contains the data type of respective column.
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Get Data types of Dataframe columns as dictionary
We can convert the Series object returned by Dataframe.dtypes to a dictionary too,
# Get a Dictionary containing the pairs of column names & data type objects.
dataTypeDict = dict(empDfObj.dtypes)
print('Data type of each column of Dataframe :')
print(dataTypeDict)
Output:
Data type of each column of Dataframe :
{'Name': dtype('O'), 'Age': dtype('int64'), 'City': dtype('O'), 'Marks': dtype('float64')}
Get the Data type of a single column in Dataframe
We can also fetch the data type of a single column from series object returned by Dataframe.dtypes i.e.
# get data type of column 'Age'
dataTypeObj = empDfObj.dtypes['Age']
print('Data type of each column Age in the Dataframe :')
print(dataTypeObj)
Output
Data type of each column Age in the Dataframe : int64
Check if data type of a column is int64 or object etc.
Using Dataframe.dtypes we can fetch the data type of a single column and can check its data type too i.e.
Check if Data type of a column is int64 in Dataframe
# Check the type of column 'Age' is int64
if dataTypeObj == np.int64:
print("Data type of column 'Age' is int64")
Output
Data type of column 'Age' is int64
Check if Data type of a column is object i.e. string in Dataframe
# Check the type of column 'Name' is object i.e string
if empDfObj.dtypes['Name'] == np.object:
print("Data type of column 'Name' is object")
Output
Data type of column 'Name' is object
Get list of pandas dataframe column names based on data type
Suppose we want a list of column names whose data type is np.object i.e string. Let’s see how to do that,
# Get columns whose data type is object i.e. string filteredColumns = empDfObj.dtypes[empDfObj.dtypes == np.object] # list of columns whose data type is object i.e. string listOfColumnNames = list(filteredColumns.index) print(listOfColumnNames)
Output
['Name', 'City']
We basically filtered the series returned by Dataframe.dtypes by value and then fetched index names i.e. columns names from this filtered series.
Get data types of a dataframe using Dataframe.info()
Dataframe.info() prints a detailed summary of the dataframe. It includes information like
- Name of columns
- Data type of columns
- Rows in dataframe
- non null entries in each column
Let’s see an example,
# Print complete details about the data frame, it will also print column count, names and data types. empDfObj.info()
Output
<class 'pandas.core.frame.DataFrame'> RangeIndex: 7 entries, 0 to 6 Data columns (total 4 columns): Name 7 non-null object Age 7 non-null int64 City 7 non-null object Marks 7 non-null float64 dtypes: float64(1), int64(1), object(2) memory usage: 208.0+ bytes
It also gives us detail about data types of columns in our dataframe.
Complete example is as follows,
import pandas as pd
import numpy as np
def main():
# List of Tuples
empoyees = [('jack', 34, 'Sydney', 155),
('Riti', 31, 'Delhi', 177.5),
('Aadi', 16, 'Mumbai', 81),
('Mohit', 31, 'Delhi', 167),
('Veena', 12, 'Delhi', 144),
('Shaunak', 35, 'Mumbai', 135),
('Shaun', 35, 'Colombo', 111)
]
# Create a DataFrame object
empDfObj = pd.DataFrame(empoyees, columns=['Name', 'Age', 'City', 'Marks'])
print("Contents of the Dataframe : ")
print(empDfObj)
print('*** Get the Data type of each column in Dataframe ***')
# Get a Series object containing the data type objects of each column of Dataframe.
# Index of series is column name.
dataTypeSeries = empDfObj.dtypes
print('Data type of each column of Dataframe :')
print(dataTypeSeries)
# Get a Dictionary containing the pairs of column names & data type objects.
dataTypeDict = dict(empDfObj.dtypes)
print('Data type of each column of Dataframe :')
print(dataTypeDict)
print('*** Get the Data type of a single column in Dataframe ***')
# get data type of column 'Age'
dataTypeObj = empDfObj.dtypes['Age']
print('Data type of each column Age in the Dataframe :')
print(dataTypeObj)
print('*** Check if Data type of a column is int64 or object etc in Dataframe ***')
# Check the type of column 'Age' is int64
if dataTypeObj == np.int64:
print("Data type of column 'Age' is int64")
# Check the type of column 'Name' is object i.e string
if empDfObj.dtypes['Name'] == np.object:
print("Data type of column 'Name' is object")
print('** Get list of pandas dataframe columns based on data type **')
# Get columns whose data type is object i.e. string
filteredColumns = empDfObj.dtypes[empDfObj.dtypes == np.object]
# list of columns whose data type is object i.e. string
listOfColumnNames = list(filteredColumns.index)
print(listOfColumnNames)
print('*** Get the Data type of each column in Dataframe using info() ***')
# Print complete details about the data frame, it will also print column count, names and data types.
empDfObj.info()
if __name__ == '__main__':
main()
Output:
Contents of the Dataframe :
Name Age City Marks
0 jack 34 Sydney 155.0
1 Riti 31 Delhi 177.5
2 Aadi 16 Mumbai 81.0
3 Mohit 31 Delhi 167.0
4 Veena 12 Delhi 144.0
5 Shaunak 35 Mumbai 135.0
6 Shaun 35 Colombo 111.0
*** Get the Data type of each column in Dataframe ***
Data type of each column of Dataframe :
Name object
Age int64
City object
Marks float64
dtype: object
Data type of each column of Dataframe :
{'Name': dtype('O'), 'Age': dtype('int64'), 'City': dtype('O'), 'Marks': dtype('float64')}
*** Get the Data type of a single column in Dataframe ***
Data type of each column Age in the Dataframe :
int64
*** Check if Data type of a column is int64 or object etc in Dataframe ***
Data type of column 'Age' is int64
Data type of column 'Name' is object
** Get list of pandas dataframe columns based on data type **
['Name', 'City']
*** Get the Data type of each column in Dataframe using info() ***
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7 entries, 0 to 6
Data columns (total 4 columns):
Name 7 non-null object
Age 7 non-null int64
City 7 non-null object
Marks 7 non-null float64
dtypes: float64(1), int64(1), object(2)
memory usage: 208.0+ bytes
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