# (Python Data Visualisation Tutorials)

# 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__)
```

## How to do Cross Correlation plot in Python

Download Code:

```
import statsmodels.tsa.stattools as stattools
/* Import Data */
df = pd.read_csv('mortality.csv')
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()
```

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

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