# Pandas Convert String to Int A Guide for Data Scientists

## Table of Contents

- Introduction to Pandas
- Converting String to Int using Pandas
- Handling Missing Values
- Handling Non-Numeric Strings
- 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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