Replacing Specific Elements

The next step we need to do is to replace dots. This task is a bit harder than the previous one, since you will replace only specific elements.

First, let's remind how to select specific rows and columns based on some condition. It can be done by applying the .loc[] property. The first parameter is either row numbersm, or condition; the second one is column names. For instance, let's get the rows containing only dot characters . within the 'morgh' column.


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# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data1.csv')
# Output only dot values within the 'morgh' column
print(df.loc[df.morgh == '.', 'morgh'])

Since we accessed the necessary rows, we can easily replace them by reassigning. We are going to repalce all the dots by NA values (nan from NumPy) and then convert the resulting column to float type (NA doesn't support int type, float only).


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# Importing libraries
import pandas as pd
import numpy as np

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data1.csv')
# Perform a replacement
df.loc[df.morgh == '.', 'morgh'] = np.nan
# Converting
df.morgh = df.morgh.astype(float)
print(df.morgh)

As you can see, the column is now considered to have float type, which means you can apply numerical methods to it (i.e., you can calculate mean, min, max, etc.).

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