Get Count of dtypes in a Pandas DataFrame

In this tutorial, we will look at how to get a count of the dtypes (data types of columns) in a pandas dataframe with the help of some examples.

How to get the datatype for each column in a Pandas Dataframe?

You can use the pandas dataframe dtypes attribute to get the data type of each column in a dataframe. The following is the syntax –

df.dtypes

Let’s look at an example.

import pandas as pd
from sklearn.datasets import load_wine

# load the wine dataset
data = load_wine(as_frame=True)
df = data['frame']
# dtype of each column
print(df.dtypes)

Output:

alcohol                         float64
malic_acid                      float64
ash                             float64
alcalinity_of_ash               float64
magnesium                       float64
total_phenols                   float64
flavanoids                      float64
nonflavanoid_phenols            float64
proanthocyanins                 float64
color_intensity                 float64
hue                             float64
od280/od315_of_diluted_wines    float64
proline                         float64
target                            int64
dtype: object

Here we load the wine dataset using sklearn.datasets and then proceed to see the dtypes of all the columns in the dataframe using the dataframe’s dtypes attribute.

Alternatively, you can also use the dataframe info() function to see the data type of columns in a dataframe.

# dataframe info
print(df.info())

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 178 entries, 0 to 177
Data columns (total 14 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   alcohol                       178 non-null    float64
 1   malic_acid                    178 non-null    float64
 2   ash                           178 non-null    float64
 3   alcalinity_of_ash             178 non-null    float64
 4   magnesium                     178 non-null    float64
 5   total_phenols                 178 non-null    float64
 6   flavanoids                    178 non-null    float64
 7   nonflavanoid_phenols          178 non-null    float64
 8   proanthocyanins               178 non-null    float64
 9   color_intensity               178 non-null    float64
 10  hue                           178 non-null    float64
 11  od280/od315_of_diluted_wines  178 non-null    float64
 12  proline                       178 non-null    float64
 13  target                        178 non-null    int64  
dtypes: float64(13), int64(1)
memory usage: 19.6 KB
None

We get the column names and their respective dtypes along with other information about the dataframe.

Count of all dtypes in Pandas Dataframe

You can use the pandas DataFrame.dtypes.value_counts() function to get a count of each dtype in the dataframe. Here, we apply the pandas value_counts() function to the pandas series returned by the dtypes attribute. The following is the syntax –

# count of each dtype in dataframe
df.dtypes.value_counts()

Let’s look at an example. Let’s apply the above method to the wine dataset that we have loaded.

# count of each dtype in the wine dataset
df.dtypes.value_counts()

Output:

float64    13
int64       1
dtype: int64

We get the count of different column data types present in the dataframe. You can see that there are 13 columns with the type float64 and one column with the type int64.

Let’s look at another example. This time, we create a dataframe from scratch.

import pandas as pd

# create a dataframe of GRE scores of two students
df = pd.DataFrame({
    'Name': ['Jim', 'Jim', 'Jim', 'Pam', 'Pam'],
    'Attempt': ['First', 'Second', 'Third', 'First', 'Second'],
    'GRE Score': [298, 321, 314, 318, 330]
})
# display the dataframe dtypes
print(df.dtypes)
print("--------")
# display the dtypes counts
print(df.dtypes.value_counts())

Output:

Name         object
Attempt      object
GRE Score     int64
dtype: object
--------
object    2
int64     1
dtype: int64

Here we create a dataframe of GRE scores of two students. You can see that the “Name” and the “Attempt” columns are of object type while the “GRE Score” column is of int64 type.

You might also be interested in –

• Pandas – Percentage of Missing Values in Each Column
• Pandas – Count Missing Values in Each Column