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 particular column.
print(my_data['Avg_mark'].dtypes)
Output
float64
Different Data types ( dtypes )
dtype | Uses | Code |
---|---|---|
int64 | Integer type | 'ID':[1,2,3,4,5,6] |
float64 | Decimal,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' ... ] |
object | String or mixed | 'NAME':['Ravi','Raju','Alex','Ron','King','Jack'] |
bool | Boolean | 'Exam':[True,False,True,True,False,False] |
timedelta64[ns] | Timedelta | pd.Series([pd.Timedelta(days=i) for i in range(6)]) |
category | Categorical | pd.Series(["a", "b", "c", "a"], dtype="category") |
Author
🎥 Join me live on YouTubePassionate about coding and teaching, I publish practical tutorials on PHP, Python, JavaScript, SQL, and web development. My goal is to make learning simple, engaging, and project‑oriented with real examples and source code.