# Python for Basic Data Analysis: PD.12 Data visualisation

Get started on your learning journey towards data science using Python. Equip yourself with practical skills in Python programming for the purpose of basic data manipulation and analysis.

## Data Visualisation

Since Pandas simply helps us with data structuring, we will need to employ Pandas in conjunction with other modules to help visualize this data. Two common modules are seaborn and matplotlib. With seaborn and matplotlib we can create some visualisations quickly using the following functions

1. Line plots

```sns.lineplot(x=X_FIEL',y=Y_FIELD,data=DATFRAME)
```

2. Regression plots

`ay=sns.relplot(x=X_FIELD,y=Y_FIELD,hue=DATA_CLASSIFIER,data=DATFRAME)`

3. Histogram Plots

`plt.hist(X_FIELD, bins=NUMBER_OF_BINS)`

4.  Pair Plots

`sns.pairplot(DATAFRAME, hue=DATA_CLASSIFIER,height=HEIGHT)`

## Activity: Data visualisation

Go ahead and try to plot these graphs using the retail dataset.

1. Plot a line plot of net_sales against date, is there any correlation?

2. Perform a regression analysis between average_selling_price and avg_margins and see if there is any correlation

3. Plot a histogram of revenue across 50 bins

4. Perform a pairplot, classify the data by order_fufilled status

## Activity: Data visualization

Go ahead and try to plot these graphs using the retail dataset.

1. Plot a line plot of net_sales against date, is there any correlation?

2. Perform a regression analysis between average_selling_price and avg_margins and see if there is any correlation

3. Plot a histogram of revenue across 50 bins

4. Perform a pairplot, classify the data by order_fufilled status

```import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

#1. Plot a line plot of net_sales against date, is there any correlation?
sns.lineplot(x='date',y='net_sales',data=df)
plt.show()
print("done")
#2. Perform a regression analysis between average_selling_price and avg_margins and see if there is any correlation
sns.regplot(x='average_selling_price',y='avg_margins',data=df)
#3. Plot a histogram of revenue across 50 bins
plt.hist(df.revenue, bins=50)
#4. Perform a pairplot, classify the data by order_fufilled status
sns.pairplot(df, hue='order_fufilled',height=3)
plt.show()```