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Pandas Convert String to Int A Guide for Data Scientists

As a data scientist you often work with datasets that contain strings that represent numeric values While these strings may look like numbers they are not recognized as such by Pythons builtin functions If youre working with the Pandas library you can easily convert these strings to integers using a few simple steps In this article we will explore how to convert string to int using Pandas
By Saturn Cloud | | Miscellaneous | Updated:

Table of Contents

  1. Introduction to Pandas
  2. Converting String to Int using Pandas
  3. Handling Missing Values
  4. Handling Non-Numeric Strings
  5. Conclusion

Introduction to Pandas

Pandas is a popular data manipulation library in Python. It provides data structures for efficiently storing and manipulating large datasets, as well as tools for data cleaning, filtering, and transformation. Pandas is built on top of the NumPy library and is a key tool for data scientists and software engineers working with Python.

Converting String to Int using Pandas

To convert a string to an integer using Pandas, you can use the astype() method. This method is available on Pandas Series and DataFrame objects and can be used to convert the data type of a column from one type to another.

Let’s start by creating a simple DataFrame that contains a column of strings representing numeric values:

import pandas as pd

df = pd.DataFrame({'numbers': ['1', '2', '3', '4', '5']})

print(df['numbers'].dtype)

Output:

Object

This DataFrame contains a single column called ‘numbers’ with five rows of strings that represent numeric values.

To convert the ‘numbers’ column to integers, we can use the astype() method as follows:

df['numbers'] = df['numbers'].astype(int)
print(df['numbers'].dtype)

Output:

int32

This code converts the ‘numbers’ column from a string data type to an integer data type.

Handling Missing Values

If the ‘numbers’ column contains missing values, such as NaN, we can use the fillna() method to fill these values with a default value before converting to integers.

df = pd.DataFrame({'numbers': ['1', '2', '3', '4', 'NaN']})
df['numbers'] = df['numbers'].replace('NaN', pd.NA).fillna(0).astype(int)
print(df['numbers'].dtype)

Output:

int32

In this example, we have used replace and fillna() to replace missing values with ‘0’ before converting to integers.

Handling Non-Numeric Strings

If the ‘numbers’ column contains non-numeric strings, such as ‘NaN’ or ‘None’, the astype() method will raise an error. To handle this, we can use the to_numeric() method, which can convert strings to numeric values while also handling non-numeric strings.

df = pd.DataFrame({'numbers': ['1', '2', '3', '4', 'None']})
df['numbers'] = pd.to_numeric(df['numbers'], errors='coerce')
print(df)

Output:

   numbers
0      1.0
1      2.0
2      3.0
3      4.0
4      NaN

In this example, we have added a ‘None’ value to the ‘numbers’ column. When we try to convert this column to integers using astype(), we will get a ValueError. However, if we use to_numeric() with the errors='coerce' parameter, non-numeric values will be converted to NaN values, which can be handled more easily.

Conclusion

In this article, we have explored how to convert string to int using Pandas. We have seen how to handle non-numeric strings and missing values, and we have learned how to use the astype() and to_numeric() methods to convert data types. By mastering these techniques, data scientists and software engineers can more effectively manipulate and analyze datasets in Python using Pandas.

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