How to Extract Numeric Values from String Columns in Pandas?

Pandas Data Manipulation: Extracting Numbers from String Columns

When working with data frames in Pandas, it becomes necessary to perform various manipulations to extract meaningful information. One such task might involve extracting numeric values from strings contained in a data frame column. Here, we explore a specific scenario to address this requirement.

Consider the following data frame with a column named 'A' containing string values:

<code class="python">import pandas as pd
import numpy as np
df = pd.DataFrame({'A':['1a',np.nan,'10a','100b','0b'],
                   })
print(df)</code>

The objective is to extract only the numbers from each cell in the 'A' column, resulting in a new data frame where the 'A' column contains only numeric values.

To achieve this, one can leverage the powerful str.extract function in Pandas. By employing a regex capture group within the str.extract expression, it becomes possible to isolate and extract the digits from each string in the data frame:

<code class="python">df.A.str.extract('(\d+)')</code>

The regex pattern '(d )' matches one or more digits (d) and captures them as a group (the parentheses ( and )). Running the above code yields the following result:

<code class="python">    A
0   1
1   NaN
2   10
3   100
4   0
Name: A, dtype: object</code>

The original strings in the 'A' column are successfully converted to numeric values, while NaN is retained for cells with missing values. This method proves particularly effective for extracting whole numbers from strings, making it a valuable tool in data analysis and manipulation scenarios.

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