Removing Characters: Method 2
As mentioned, there are two ways to remove a character from all column values. The second method uses a lambda function. How does it work? You define a lambda function that removes a certain character/characters from a function variable, and apply it to the selected column. Then you convert obtained values to the necessary type and save them.
Tarea
Swipe to start coding
- Define a lambda function with a single argument
xthat will look for and delete any of the characters'$°C%'from both the left and right sides (using the.strip()method). Assign the function to thermvariable. - Apply the
rmfunction to the'Fuel_Price'column and then convert it to numerical type (float) using the.astype()method. Assign the obtained result to the same column. - Perform the same actions described in the step 2 for the
'Unemployment'column. - Perform the same actions described in the step 2 for the
'Temperature'column. - Display the first row of the
dfdataframe and data types of thedfdataframe.
Solución
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# Load the library
import pandas as pd
# Read the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data_init.csv')
# Define a lambda function
rm = lambda x: x.strip('$°C%')
# Convert the 'Fuel_Price' column into numerical
df['Fuel_Price'] = df['Fuel_Price'].apply(rm).astype(float)
# Convert the 'Unemployment' column into numerical
df['Unemployment'] = df['Unemployment'].apply(rm).astype(float)
# Convert the 'Temperature' column into numerical
df['Temperature'] = df['Temperature'].apply(rm).astype(float)
# Display the first row of dataframe and dtypes
print(df.head(1))
print(df.dtypes)
¿Todo estuvo claro?
¡Gracias por tus comentarios!
Sección 1. Capítulo 5
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# Load the library
import pandas as pd
# Read the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data_init.csv')
# Define a lambda function
rm = ___ ___: x.___('___')
# Convert the 'Fuel_Price' column into numerical
df['Fuel_Price'] = df['___'].apply(___).___(float)
# Convert the 'Unemployment' column into numerical
df['Unemployment'] = df['Unemployment'].___(___).astype(___)
# Convert the 'Temperature' column into numerical
df['___'] = df['___'].___(rm).astype(___)
# Display the first row of dataframe and dtypes
print(df.___(___))
print(df.___)
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