Зміст курсу
Analyzing and Visualizing Real-World Data
Analyzing and Visualizing Real-World Data
2. Preprocessing Data: Part II
First Look at the Data
Welcome to the last section! In the previous section, we investigated that the most profitable week, according to sales data, is the 'pre-Christmas' week, while Christmas week itself is significantly worse.
We want to start with some exploratory analysis: let's see revenues over weeks using matplotlib
and seaborn
.
Завдання
- Import the
matplotlib.pyplot
with the aliasplt
, andseaborn
with the aliassns
. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
df
dataframe by the'Date'
column, select the'Weekly_Sales'
column, calculate total values, and reset indexes. Save the obtained data within thedata
variable. - Initialize a line plot with the
'Date'
values on the x-axis,'Weekly_Sales'
values on the y-axis, using thedata
dataframe. - Display the plot.
Завдання
- Import the
matplotlib.pyplot
with the aliasplt
, andseaborn
with the aliassns
. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
df
dataframe by the'Date'
column, select the'Weekly_Sales'
column, calculate total values, and reset indexes. Save the obtained data within thedata
variable. - Initialize a line plot with the
'Date'
values on the x-axis,'Weekly_Sales'
values on the y-axis, using thedata
dataframe. - Display the plot.
Все було зрозуміло?
First Look at the Data
Welcome to the last section! In the previous section, we investigated that the most profitable week, according to sales data, is the 'pre-Christmas' week, while Christmas week itself is significantly worse.
We want to start with some exploratory analysis: let's see revenues over weeks using matplotlib
and seaborn
.
Завдання
- Import the
matplotlib.pyplot
with the aliasplt
, andseaborn
with the aliassns
. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
df
dataframe by the'Date'
column, select the'Weekly_Sales'
column, calculate total values, and reset indexes. Save the obtained data within thedata
variable. - Initialize a line plot with the
'Date'
values on the x-axis,'Weekly_Sales'
values on the y-axis, using thedata
dataframe. - Display the plot.
Завдання
- Import the
matplotlib.pyplot
with the aliasplt
, andseaborn
with the aliassns
. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
df
dataframe by the'Date'
column, select the'Weekly_Sales'
column, calculate total values, and reset indexes. Save the obtained data within thedata
variable. - Initialize a line plot with the
'Date'
values on the x-axis,'Weekly_Sales'
values on the y-axis, using thedata
dataframe. - Display the plot.
Все було зрозуміло?
First Look at the Data
Welcome to the last section! In the previous section, we investigated that the most profitable week, according to sales data, is the 'pre-Christmas' week, while Christmas week itself is significantly worse.
We want to start with some exploratory analysis: let's see revenues over weeks using matplotlib
and seaborn
.
Завдання
- Import the
matplotlib.pyplot
with the aliasplt
, andseaborn
with the aliassns
. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
df
dataframe by the'Date'
column, select the'Weekly_Sales'
column, calculate total values, and reset indexes. Save the obtained data within thedata
variable. - Initialize a line plot with the
'Date'
values on the x-axis,'Weekly_Sales'
values on the y-axis, using thedata
dataframe. - Display the plot.
Завдання
- Import the
matplotlib.pyplot
with the aliasplt
, andseaborn
with the aliassns
. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
df
dataframe by the'Date'
column, select the'Weekly_Sales'
column, calculate total values, and reset indexes. Save the obtained data within thedata
variable. - Initialize a line plot with the
'Date'
values on the x-axis,'Weekly_Sales'
values on the y-axis, using thedata
dataframe. - Display the plot.
Все було зрозуміло?
Welcome to the last section! In the previous section, we investigated that the most profitable week, according to sales data, is the 'pre-Christmas' week, while Christmas week itself is significantly worse.
We want to start with some exploratory analysis: let's see revenues over weeks using matplotlib
and seaborn
.
Завдання
- Import the
matplotlib.pyplot
with the aliasplt
, andseaborn
with the aliassns
. - Prepare data for visualization: calculate the total revenue for all shops across weeks. To do it, group the values of the
df
dataframe by the'Date'
column, select the'Weekly_Sales'
column, calculate total values, and reset indexes. Save the obtained data within thedata
variable. - Initialize a line plot with the
'Date'
values on the x-axis,'Weekly_Sales'
values on the y-axis, using thedata
dataframe. - Display the plot.