The singular form dtype is used to check the data type for a single column. And the plural form dtypes is for data frame which returns data types for all columns. Essentially:
For a single column:
dataframe.column.dtype
For all columns:
dataframe.dtypes
Example:
import pandas as pd
df = pd.DataFrame({'A': [1,2,3], 'B': [True, False, False], 'C': ['a', 'b', 'c']})
df.A.dtype
# dtype('int64')
df.B.dtype
# dtype('bool')
df.C.dtype
# dtype('O')
df.dtypes
#A int64
#B bool
#C object
#dtype: object
Suppose df is a pandas DataFrame then to get number of non-null values and data types of all column at once use:
df.info()
To go one step further, I assume you want to do something with these dtypes.
df.dtypes.to_dict() comes in handy.
my_type = 'float64' #<---
dtypes = dataframe.dtypes.to_dict()
for col_nam, typ in dtypes.items():
if (typ != my_type): #<---
raise ValueError(f"Yikes - `dataframe['{col_name}'].dtype == {typ}` not {my_type}")
You'll find that Pandas did a really good job comparing NumPy classes and user-provided strings. For example: even things like 'double' == dataframe['col_name'].dtype will succeed when .dtype==np.float64.
Source: Stackoverflow.com