Fill Pandas Column with One Value - Python Tutorial
Introduction
When working with data in Python, the Pandas library is an essential tool for data manipulation and analysis. One common task when working with Pandas data frames is filling a column with a single value. In this tutorial, we will explore various ways to accomplish this task.
Method 1: Assigning a Scalar Value
The simplest way to fill a Pandas column with a single value is by assigning a scalar value to the column. This can be done using the Pandas df['column_name'] = value
syntax. For example, to fill a column named 'new_col' with the value 5, we can use the following code:
import pandas as pd
# create a data frame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# fill the column 'new_col' with the value 5
df['new_col'] = 5
print(df)
This will produce the following output:
A B new_col
0 1 4 5
1 2 5 5
2 3 6 5
Method 2: Using Numpy's Full Function
Another way to fill a Pandas column with a single value is by using Numpy's full()
function. This function creates an array with a specified shape and fills it with a specified value. We can use this function to create an array with the same shape as the column we want to fill, and then assign it to the column. For example, to fill a column named 'new_col' with the value 5, we can use the following code:
import pandas as pd
import numpy as np
# create a data frame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
# create an array with the same shape as the 'new_col' column
fill_value = np.full(df.shape[0], 5)
# assign the array to the 'new_col' column
df['new_col'] = fill_value
print(df)
This will produce the same output as Method 1.
Conclusion
In this tutorial, we explored two ways to fill a Pandas column with a single value. The first method is by assigning a scalar value to the column using the Pandas df['column_name'] = value
syntax. The second method is by using Numpy's full()
function to create an array with the same shape as the column and then assigning it to the column. Both methods are simple and effective for filling a Pandas column with a single value.
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