Comparing Dynamics
Returning to the previous section, you solved the problem of finding the most profitable stores. According to the data, these stores are numbered 20, 4, 14, 13, and 2 (the numbers are saved in the top_stores variable). Let's examine the sales dynamics of these stores.
Завдання
Swipe to start coding
- Prepare the data for visualization: filter the values in the
dfso that only data for stores with the numbers present in thetop_storeslist remain. Save the resulting data in thedatavariable. - Initialize a line plot with dates on the x-axis, weekly sales on the y-axis, using the
datadataframe. Display a separate line for each store.
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# Loading the library
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Reading the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data3.csv')
df['Date'] = pd.to_datetime(df['Date'], dayfirst = True)
# Preparing the data
top_stores = [20, 4, 14, 13, 2]
data = df.loc[df['Store'].isin(top_stores)]
# Initializing the plot
plt.figure(figsize = (15, 3))
sns.lineplot(x = 'Date', y = 'Weekly_Sales', hue = 'Store', data = data)
# Displaying the plot
plt.show()
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Секція 4. Розділ 2
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# Loading the library
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Reading the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data3.csv')
df['Date'] = pd.to_datetime(df['Date'], dayfirst = True)
# Preparing the data
top_stores = [20, 4, 14, 13, 2]
data = df.___[___.___(___)]
# Initializing the plot
plt.figure(figsize = (15, 3))
sns.___(x = 'Date', y = 'Weekly_Sales', hue = '___', data = data)
# Displaying the plot
plt.show()
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