Data set correlation coefficient calculation using Python | S-Logix

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How to Calculate Correlation Coefficient for a Dataset

Calculate Correlation Coefficient

Condition for Calculating Correlation Coefficient for a Dataset

Description:
The correlation coefficient is a statistical measure that describes the strength and direction of the relationship between two variables. The most commonly used correlation coefficient is the Pearson correlation coefficient, which ranges from -1 to 1:
- 1 indicates a perfect positive correlation.
- -1 indicates a perfect negative correlation.
- 0 indicates no correlation.

Step-by-Step Process

Import the Required Libraries:
Import Pandas for data manipulation and also import NumPy or Matplotlib for more advanced analysis or visualization.
Prepare the Data:
Ensure the data is in a numerical format (integer or float) for correlation calculation.
Use the .corr() Method:
Apply the .corr() method to the DataFrame to calculate the correlation matrix, or to two specific columns to compute the correlation coefficient between them.
Interpret the Results:
Analyze the correlation coefficient to determine the relationship between the variables.

Sample Source Code

import pandas as pd

data = {
"Height": [5.5, 6.0, 5.7, 5.8, 6.2],
"Weight": [150, 160, 155, 158, 170],
"Age": [25, 30, 28, 35, 40]
}
df = pd.DataFrame(data)

correlation_matrix = df.corr()

print(correlation_matrix)

print()

# Calculate correlation between 'Height' and 'Weight'
print("Calculate correlation between 'Height' and 'Weight")

correlation_height_weight = df['Height'].corr(df['Weight'])

print("Correlation between Height and Weight:", correlation_height_weight)

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