Or is it Holiday Effect?

Let's find out if holidays could be the cause. To do this, we will display the dates next to the points.

Compito

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Add a hue parameter to the .scatterplot() function so that the points will be colored in accordance to the values in the 'Holiday_Flag' column.
Initialize a for loop to display the dates next to the points. Iterate over the indexes of data using index as the dummy variable.
Within the loop, use the .text() method of plt to display text on the plot. The method should have 4 parameters:
- the first is responsible for the x-coordinate of the text, which should be the index value of the 'Temperature' column with a 0.2 offset;
- the second is responsible for the y-coordinate of text, which should be the index value of the 'Weekly_Sales' column;
- the third is the text that should be displayed which should be the index value of the 'Date' column, transformed into a date;
- the fourth is the color parameter, responsible for color of the text, which should be 'black'.

# 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)) & (df.Temperature >= -10)

& (df.Temperature <= 10) & (df.Weekly_Sales >= 3000000)]

# Initializing the plot

sns.scatterplot(x = 'Temperature', y = 'Weekly_Sales', hue = 'Holiday_Flag', data = data)

# Add Date labels to points

for index in data.index:

plt.text(data.Temperature[index] + 0.2, data.Weekly_Sales[index],

data.Date[index].date(), color = 'black')

# Displaying the plot

plt.show()

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Sezione 4. Capitolo 5

# 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)) & (df['Temperature'] >= -10)

& (df['Temperature']) & (df['Weekly_Sales'] >= 3000000)]

# Initializing the plot

sns.scatterplot(x = 'Temperature', y = 'Weekly_Sales', ___, data = data)

# Add Date labels to points

for index in data.___:

plt.text(___[index] + 0.2, data___[index],

data___[index].date(), color = 'black')

# Displaying the plot

plt.show()

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