Pandas How to Check Dtype for All Columns in a Dataframe?

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_name, 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.

If you have a lot many columns and you do df.info() or df.dtypes it may give you overall statistics of columns or just some columns from the top and bottom like

<class 'pandas.core.frame.DataFrame'>

Int64Index: 4387 entries, 1 to 4387

Columns: 119 entries, 
CoulmnA to ColumnZ

dtypes: datetime64[ns(24), 
float64(54), object(41)

memory usage: 4.0+ MB

It just gives that 24 columns are datetime, 54 are float64 and 41 are object.

So, if you want the datatype of each column in one command, do:

dict(df.dtypes)

(This answer does not directly answer OP's question but might be useful.)

Responses so far rely on printed reports or string values, so might not be future-proof.

pandas offers programmatic ways for type checking:

import pandas as pd
from pandas.api.types import is_object_dtype, is_numeric_dtype, is_bool_dtype
df = pd.DataFrame({'A': [1,2,3], 'B': [True, False, False], 'C': ['a', 'b', 'c']})

is_numeric_dtype(df['A'])
>>> True