Pandas: Specify dtypes when Importing CSV File?

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You can use the following basic syntax to specify the dtype of each column in a DataFrame when importing a CSV file into pandas:

df = pd.read_csv('my_data.csv',
                 dtype = {'col1': str, 'col2': float, 'col3': int})

The dtype argument specifies the data type that each column should have when importing the CSV file into a pandas DataFrame.

The following example shows how to use this syntax in practice.

Example: Specify dtypes when Importing CSV File into Pandas

Suppose we have the following CSV file called basketball_data.csv:

If we import the CSV file using the read_csv() function, pandas will attempt to identify the data type for each column automatically:

import pandas as pd

#import CSV file
df = pd.read_csv('basketball_data.csv')

#view resulting DataFrame
print(df)

   A  22  10
0  B  14   9
1  C  29   6
2  D  30   2
3  E  22   9
4  F  31  10

#view data type of each column
print(df.dtypes)

team        object
points       int64
rebounds     int64
dtype: object

From the output we can see that the columns in the DataFrame have the following data types:

• team: object
• points: int64
• rebounds: int64

However, we can use the dtype argument within the read_csv() function to specify the data types that each column should have:

import pandas as pd

#import CSV file and specify dtype of each column
df = pd.read_csv('basketball_data.csv',
                 dtype = {'team': str, 'points': float, 'rebounds': int}))

#view resulting DataFrame
print(df)

   A  22  10
0  B  14   9
1  C  29   6
2  D  30   2
3  E  22   9
4  F  31  10

#view data type of each column
print(df.dtypes)

team         object
points      float64
rebounds      int32
dtype: object

From the output we can see that the columns in the DataFrame have the following data types:

• team: object
• points: float64
• rebounds: int32

These data types match the ones that we specified using the dtype argument.

Note that in this example, we specified the dtype for each column in the DataFrame.

Note: You can find the complete documentation for the pandas read_csv() function .