Challenge
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Now, you know how to work with time-series data, and you are faced with the task of processing it:
- Convert column data type from str
'Month'to datetime. - Fill
NaNvalues using the interpolation method. - Use the moving average method to remove noise from the data.
Oplossing
import pandas as pd
# Read a dataset
dataset = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/monthly-sunspots.csv')
# Convert 'Month' column to datetime format
dataset['Month'] = pd.to_datetime(dataset['Month'], format='%Y-%m')
# Interpolate missing values
dataset['Sunspots'] = dataset['Sunspots'].interpolate(method='linear')
# Remove noise from the data
dataset['Sunspots'] = dataset['Sunspots'].rolling(window=3).mean()
# Print the transformed dataset
print(dataset)
Was alles duidelijk?
Bedankt voor je feedback!
Sectie 4. Hoofdstuk 6
import pandas as pd
# Read a dataset
dataset = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/9c23bf60-276c-4989-a9d7-3091716b4507/datasets/monthly-sunspots.csv')
# Convert 'Month' column to datetime format
dataset['Month'] = pd.___(dataset['Month'], format=___)
# Interpolate missing values
dataset['Sunspots'] = dataset['Sunspots'].___(method='linear')
# Remove noise from the data
dataset['Sunspots'] = dataset['Sunspots'].___(window=3).mean()
# Print the transformed dataset
print(dataset)
