CODEWITHSUNDEEP
pythonpython-3.xpandasdataframe
user3768495
user3768495

Reputation: 4637

pandas how to check dtype for all columns in a dataframe?

It seems that dtype only work for pandas.DataFrame.Series, right? Is there a function to display data types of all columns at once?

Upvotes: 235

Views: 640564

Answers (7)

Kermit
Kermit

Reputation: 5982

To go one step further, I assume you actually want to do something with these dtypes. df.dtypes.to_dict() comes in handy.

dtypes = dataframe.dtypes.to_dict()

my_type = 'float64'

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.

Upvotes: 18

Stellar Sword
Stellar Sword

Reputation: 6226

The proper way is now:

import pandas as pd
df = pd.DataFrame({'A': [1,2,3], 'B': [True, False, False], 'C': ['a', 'b', 'c']})
df['C'] = df['C'].astype('category')
if isinstance(df['C'].dtype, pd.CategoricalDtype):
  print('xx')

Upvotes: 0

fehim
fehim

Reputation: 35

You can also see it indirectly by using dataframe_name.column_name which shows column values and also dtype with it.

Example:

import pandas as pd
data = {"strings" : ["a","b","c"], "ints" : [1,2,3]}
df = pd.DataFrame(data)
print(df.strings)
print("------------")
print(df.ints)

which will output:

0    a
1    b
2    c
Name: strings, dtype: object
------------
0    1
1    2
2    3
Name: ints, dtype: int64

Upvotes: 1

nocibambi
nocibambi

Reputation: 2421

(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

Upvotes: 14

Talha Tayyab
Talha Tayyab

Reputation: 27297

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)

Upvotes: 8

Mumtaj Ali
Mumtaj Ali

Reputation: 481

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()

Upvotes: 32

akuiper
akuiper

Reputation: 214927

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

Upvotes: 364

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