Converting Strings to Integers in Pandas DataFrame: A Comprehensive Guide
Have you ever worked with data that had strings instead of integer values? Did you find it frustrating to perform calculations on non-numeric values?
Worry no more, as Pandas DataFrame provides easy ways to convert strings to integers. In this article, we will explore the different methods to convert strings to integers and also deal with non-numeric values in the DataFrame.
Creating a DataFrame
Before we dive into the conversion methods, let’s first create a DataFrame to work with. A DataFrame is a table-like data structure used in data analysis, consisting of rows and columns.
In this example, we will create a DataFrame named ‘products’, with two columns: the ‘Name’ and ‘Price’. “`
import pandas as pd
data = {‘Name’: [‘Apple’, ‘Banana’, ‘Grapes’, ‘Pineapple’], ‘Price’: [’10’, ’25’, ’18’, ’50’]}
products = pd.DataFrame(data)
print(products)
“`
The output will be:
“`
Name Price
0 Apple 10
1 Banana 25
2 Grapes 18
3 Pineapple 50
“`
Converting the Strings to Integers
Now that we have created the DataFrame ‘products’, let’s convert the ‘Price’ column from strings to integers. There are two primary methods that you can use to perform this conversion: ‘
astype(int)’ and ‘
to_numeric’.
astype(int)
The first method is the ‘
astype(int)’ method. It is a built-in Pandas method that converts a column to a different data type.
In this case, we want to convert the ‘Price’ column from strings to integers. Here is how you can do it:
“`
products[‘Price’] = products[‘Price’].
astype(int)
print(products)
“`
The output will be:
“`
Name Price
0 Apple 10
1 Banana 25
2 Grapes 18
3 Pineapple 50
“`
Notice how the ‘Price’ values are now integers instead of strings.
to_numeric
The second method is the ‘
to_numeric’ method. This method can be used to convert a column or multiple columns to numeric data types.
Here is how you can use ‘
to_numeric’ to convert the ‘Price’ column:
“`
products[‘Price’] = pd. to_numeric(products[‘Price’], errors=’coerce’)
print(products)
“`
The output will be the same as the earlier method:
“`
Name Price
0 Apple 10
1 Banana 25
2 Grapes 18
3 Pineapple 50
“`
Notice the ‘errors’ parameter set to ‘coerce’. It is used to convert non-numeric values to NaN.
Dealing with Non-Numeric Values
Sometimes, the ‘Price’ column might have non-numeric values like empty strings or special characters. In these cases, the conversion methods above will not work.
We have to deal with non-numeric values to ensure that the column contains only integers. Here are some methods to handle non-numeric values.
Converting Strings to NaN
The first method is to convert non-numeric strings to NaN. This method is useful because NaN values can be easily replaced with other values using the ‘fillna’ method.
Here is how you can convert non-numeric strings to NaN values:
“`
products[‘Price’] = pd. to_numeric(products[‘Price’], errors=’coerce’)
print(products)
“`
Notice that the ‘errors’ parameter is set to ‘coerce’ again.
Replacing NaN Values with 0
After converting non-numeric strings to NaN values, we can then replace the NaN values with 0 values. Here’s how we can do it:
“`
import numpy as np
products[‘Price’] = products[‘Price’].replace(np.nan, 0, regex=True)
print(products)
“`
The output will be:
“`
Name Price
0 Apple 10
1 Banana 25
2 Grapes 18
3 Pineapple 50
“`
Conclusion
In summary, Pandas DataFrame provides us with powerful methods to convert strings to integers and also deal with non-numeric values. The ‘
astype(int)’ and ‘
to_numeric’ methods enable us to convert column values to integers, while the ‘replace’ and ‘fillna’ methods are useful for handling non-numeric values.
With these methods, working with non-numeric data types in the DataFrame is now a breeze. In conclusion, converting strings to integers in Pandas DataFrame is an essential skill for anyone working with data.
By using the ‘
astype(int)’ and ‘
to_numeric’ methods, you can easily convert column values to integers. Additionally, by using the ‘replace’ and ‘fillna’ methods, you can handle non-numeric values in your DataFrame.
The importance of ensuring numerical accuracy when dealing with data cannot be overstated, and these methods provide an efficient way to do so. So, remember to use these methods when working with data and always ensure numerical accuracy.