Challenge 1

Compito

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In this challenge, you will need to work with the 'adult-census.csv' dataset. It contains both categorical and numerical data. Your task will be to prepare the data for processing.

Read the dataset 'adult-census.csv'
Explore the dataset. Carefully check which character indicates the missed data in the dataset and replace it with the np.nan object
Remove rows with missing values
Let's start with processing categorical data - columns 'workclass', 'sex' Use a one-hot encoding method to encode them
For numeric data ('age', 'hours-per-week'), you will need to scale the data
Print processed data

Soluzione

from sklearn.preprocessing import StandardScaler, MinMaxScaler

import numpy as np

import pandas as pd

# Read the dataset

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/adult-census.csv')

# Replace the symbol of missed data with real np.nan object

df = df.replace(' ?', np.nan)

# Drop all rows with nan values

df = df.dropna()

# Create MinMaxScaler

scaler = MinMaxScaler()

# Make one-hot encoding with pd.get_dummies()

one_hot = pd.get_dummies(df[['workclass', 'sex']])

# Join the encoded columns

df = df.join(one_hot)

# Drop initial columns

df = df.drop(['workclass', 'sex'], axis=1)

# Fit and transform numerical data for the scaler

df[['age', 'hours-per-week']] = scaler.fit_transform(df[['age', 'hours-per-week']])

# Print new data

print(df)

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 6. Capitolo 1

from sklearn.preprocessing import StandardScaler, MinMaxScaler

import numpy as np

import pandas as pd

# Read the dataset

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/adult-census.csv')

# Replace the symbol of missed data with real np.nan object

df = df.___(___, np.nan)

# Drop all rows with nan values

df = df.___()

# Create MinMaxScaler

scaler = MinMaxScaler()

# Make one-hot encoding with pd.get_dummies()

one_hot = pd.___(df[['workclass', 'sex']])

# Join the encoded columns

df = df.___(one_hot)

# Drop initial columns

df = df.___(['workclass', 'sex'], axis=1)

# Fit and transform numerical data for the scaler

df[['age', 'hours-per-week']] = scaler.___(df[['age', 'hours-per-week']])

# Print new data

print(df)

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