First Look at the Data | Visualizing Data
Analyzing and Visualizing Real-World Data

Conteúdo do Curso

Analyzing and Visualizing Real-World Data

# 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`.

Tarefa

1. Import the `matplotlib.pyplot` with the alias `plt`, and `seaborn` with the alias `sns`.
2. 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 the `data` variable.
3. Initialize a line plot with the `'Date'` values on the x-axis, `'Weekly_Sales'` values on the y-axis, using the `data` dataframe.
4. Display the plot.

Tudo estava claro?

Seção 4. Capítulo 1

# 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`.

Tarefa

1. Import the `matplotlib.pyplot` with the alias `plt`, and `seaborn` with the alias `sns`.
2. 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 the `data` variable.
3. Initialize a line plot with the `'Date'` values on the x-axis, `'Weekly_Sales'` values on the y-axis, using the `data` dataframe.
4. Display the plot.

Tudo estava claro?

Seção 4. Capítulo 1
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