Pearson_correlation.ipynb

Kernel: Python 3 (Anaconda)

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

data = pd.read_csv('longley.csv', header=0, index_col=0)
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 16 entries, 1947 to 1962
Data columns (total 7 columns):
GNP.deflator    16 non-null float64
GNP             16 non-null float64
Unemployed      16 non-null float64
Armed.Forces    16 non-null float64
Population      16 non-null float64
Year            16 non-null int64
Employed        16 non-null float64
dtypes: float64(6), int64(1)
memory usage: 1.0 KB

data.head(5)

	GNP.deflator	GNP	Unemployed	Armed.Forces	Population	Year	Employed
1947	83.0	234.289	235.6	159.0	107.608	1947	60.323
1948	88.5	259.426	232.5	145.6	108.632	1948	61.122
1949	88.2	258.054	368.2	161.6	109.773	1949	60.171
1950	89.5	284.599	335.1	165.0	110.929	1950	61.187
1951	96.2	328.975	209.9	309.9	112.075	1951	63.221

data['Employed'].corr(data['Population'], method='pearson')

0.96039057159437557