Python Data Visualisation for Business Analyst – How to do Cross Correlation plot in Python

In this data visualisation tutorial, you will learn – How to do Cross Correlation plot in Python.

Cross correlation plot shows the lags of two time series with each other.

Setup

Run this once before the plot’s code. The individual charts, however, may redefine its own aesthetics.

``````/* !pip install brewer2mpl */
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings; warnings.filterwarnings(action='once')

large = 22; med = 16; small = 12
params = {'axes.titlesize': large,
'legend.fontsize': med,
'figure.figsize': (16, 10),
'axes.labelsize': med,
'axes.titlesize': med,
'xtick.labelsize': med,
'ytick.labelsize': med,
'figure.titlesize': large}
plt.rcParams.update(params)
plt.style.use('seaborn-whitegrid')
sns.set_style("white")
%matplotlib inline

/* Version */
print(mpl.__version__)
print(sns.__version__)``````
Data: mortality

How to do Cross Correlation plot in Python

``````import statsmodels.tsa.stattools as stattools

/* Import Data */
x = df['mdeaths']
y = df['fdeaths']

/* Compute Cross Correlations */
ccs = stattools.ccf(x, y)[:100]
nlags = len(ccs)

/* Compute the Significance level */
conf_level = 2 / np.sqrt(nlags)

/* Draw Plot */
plt.figure(figsize=(12,7), dpi= 80)

plt.hlines(0, xmin=0, xmax=100, color='gray')  # 0 axis
plt.hlines(conf_level, xmin=0, xmax=100, color='gray')
plt.hlines(-conf_level, xmin=0, xmax=100, color='gray')

plt.bar(x=np.arange(len(ccs)), height=ccs, width=.3)

/* Decoration */
plt.title('\$Cross\; Correlation\; Plot:\; mdeaths\; vs\; fdeaths\$', fontsize=22)
plt.xlim(0,len(ccs))
plt.show()``````

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