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)
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
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
Answers for Activity: Data Visualization
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
df=pd.read_csv("Retail dataset.csv")
#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()
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