How do you plot a correlation graph in python?

Surprised to see no one mentioned more capable, interactive and easier to use alternatives.

A) You can use plotly:

1. Just two lines and you get:

2. interactivity,

3. smooth scale,

4. colors based on whole dataframe instead of individual columns,

5. column names & row indices on axes,

6. zooming in,

7. panning,

8. built-in one-click ability to save it as a PNG format,

9. auto-scaling,

10. comparison on hovering,

11. bubbles showing values so heatmap still looks good and you can see values wherever you want:

import plotly.express as px
fig = px.imshow(df.corr())
fig.show()

B) You can also use Bokeh:

All the same functionality with a tad much hassle. But still worth it if you do not want to opt-in for plotly and still want all these things:

from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource, LinearColorMapper
from bokeh.transform import transform
output_notebook()
colors = ['#d7191c', '#fdae61', '#ffffbf', '#a6d96a', '#1a9641']
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
data = df.corr().stack().rename("value").reset_index()
p = figure(x_range=list(df.columns), y_range=list(df.index), tools=TOOLS, toolbar_location='below',
           tooltips=[('Row, Column', '@level_0 x @level_1'), ('value', '@value')], height = 500, width = 500)

p.rect(x="level_1", y="level_0", width=1, height=1,
       source=data,
       fill_color={'field': 'value', 'transform': LinearColorMapper(palette=colors, low=data.value.min(), high=data.value.max())},
       line_color=None)
color_bar = ColorBar(color_mapper=LinearColorMapper(palette=colors, low=data.value.min(), high=data.value.max()), major_label_text_font_size="7px",
                     ticker=BasicTicker(desired_num_ticks=len(colors)),
                     formatter=PrintfTickFormatter(format="%f"),
                     label_standoff=6, border_line_color=None, location=(0, 0))
p.add_layout(color_bar, 'right')

show(p)

Correlation means an association, It is a measure of the extent to which two variables are related. 

1. Positive Correlation: When two variables increase together and decrease together. They are positively correlated. ‘1’ is a perfect positive correlation. For example – demand and profit are positively correlated the more the demand for the product, the more profit hence positive correlation.

2. Negative Correlation: When one variable increases and the other variable decreases together and vice-versa. They are negatively correlated. For example, If the distance between magnet increases their attraction decreases, and vice-versa. Hence, a negative correlation. ‘-1’  is no correlation

3. Zero Correlation( No Correlation): When two variables don’t seem to be linked at all. ‘0’ is a perfect negative correlation. For Example, the amount of tea you take and level of intelligence.

Plotting Correlation matrix using Python

Step 1: Importing the libraries.

Python3

import sklearn

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

Step 2: Finding the Correlation between two variables.

Python3

y = pd.Series([1, 2, 3, 4, 3, 5, 4])

x = pd.Series([1, 2, 3, 4, 5, 6, 7])

correlation = y.corr(x)

correlation

Output:

Step 3: Plotting the graph. Here we are using scatter plots. A scatter plot is a diagram where each value in the data set is represented by a dot. Also, it shows a relationship between two variables.

Python3

plt.scatter(x, y)

plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))

         (np.unique(x)), color='red')

Output:

Remember the points that were explained above. Observe both the images you will find similarity Also, observe the value of the correlation is near to 1, hence the positive correlation is reflected.

Adding title and labels in the graph

Python3

plt.title('Correlation')

plt.scatter(x, y)

plt.plot(np.unique(x),

         np.poly1d(np.polyfit(x, y, 1))

         (np.unique(x)), color='red')

plt.xlabel('x axis')

plt.ylabel('y axis')

Output:

Plot using Heatmaps

There are many ways you can plot correlation matrices one efficient way is using the heatmap. It is very easy to understand the correlation using heatmaps it tells the correlation of one feature(variable) to every other feature(variable). In other words, A correlation matrix is a tabular data representing the ‘correlations’ between pairs of variables in a given data.

Python3

import seaborn as sns

flights = sns.load_dataset("flights")

ax = sns.heatmap(flights.corr(), annot=True)

Output:

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