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Oppiskele Finding the Correlation | Extracting Data
Advanced Techniques in pandas

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Finding the Correlation

Finally, let's move to the last method of this section called .corr(). It helps out a lot to find the relationship between numerical data. Imagine that you have a dataset on houses:

Let's examine the output of the data.corr() in our case:

So, let's do it step by step: You have vertical and horizontal values; each pair overlaps. In each overlap, we can receive a value from -1 to 1.

  • 1 means that two values depend on each other in a directly proportional way (if one value increases, the other increases too);
  • -1 means that two values depend on each other in an inversely proportional way (if one value increases, the other decreases);
  • 0 means that the two dependent values aren't proportional.

Note

If the dataset contains non-numeric columns, such as in the cars.csv dataset used in the task, you should set the argument numeric_only=True to compute the correlation using only the numeric columns.

Tehtävä

Swipe to start coding

You'll end this section with an effortless task: apply the .corr() function to the dataset. Then, try to analyze the numbers you get.

Ratkaisu

import pandas as pd

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)

# Apply the `.corr()` method
print(data.corr(numeric_only=True))

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Osio 3. Luku 7
import pandas as pd

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/4bf24830-59ba-4418-969b-aaf8117d522e/cars.csv', index_col = 0)

# Apply the `.corr()` method
print(___)
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