DataFrame.dtypes()

Pandas

dtypes : Returns dtypes of DataFrame.
Here is one DataFrame with 7 types (all) of dtypes.
import pandas as pd 
td = pd.Series([pd.Timedelta(days=i) for i in range(6)])  
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'Avg_mark':[45.5,48.09,50.12,55.1,50.6,55.6],
         'dt_start':['1/1/2020','2/1/2020','5/1/2020','11/7/2020',
			'15/8/2020','31/12/2020'],
         'Exam':[True,False,True,True,False,False],
         'dt':td,
         'grade':['a', 'c', 'b', 'b','b','c']}

my_data = pd.DataFrame(data=my_dict)
my_data['grade']=my_data['grade'].astype('category')
my_data['dt_start'] = pd.to_datetime(my_data['dt_start'])
print(my_data.dtypes)
Output
NAME                 object
ID                    int64
MATH                  int64
Avg_mark            float64
dt_start     datetime64[ns]
Exam                   bool
dt          timedelta64[ns]
grade              category
dtype: object
We can get dtype of perticular column.
print(my_data['Avg_mark'].dtypes)
Output
float64
Different Data types ( dtypes )
dtypeUses Code
int64Integer type'ID':[1,2,3,4,5,6]
float64Decimal,float'Avg_mark':[45.5,48.09,50.12,55.1,50.6,55.6]
datetime64[ns]Date & Time'dt_start':['1/1/2020','2/1/2020','5/1/2020' ... ]
objectString or mixed'NAME':['Ravi','Raju','Alex','Ron','King','Jack']
boolBoolean'Exam':[True,False,True,True,False,False]
timedelta64[ns]Timedeltapd.Series([pd.Timedelta(days=i) for i in range(6)])
categoryCategoricalpd.Series(["a", "b", "c", "a"], dtype="category")
Using correct data type is important as our handling of data depends on it. If data type is int64 then we will get output as 4 for 2 + 2 , but we will get 22 as output if data type is object.

You can change the data type by using astype().
We can successfully convert the data types if data matches to new data type. Otherwise we have to clean the data before using astype()
Data Cleaning
Pandas to_timedelta() astype() select_dtypes() timedelta64()


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