Skip to content

Data Manipulation with pandas 🐼

# Import the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Import the four datasets
avocado = pd.read_csv("datasets/avocado.csv")
homelessness = pd.read_csv("datasets/homelessness.csv")
temperatures = pd.read_csv("datasets/temperatures.csv")
walmart = pd.read_csv("datasets/walmart.csv")

Explore Datasets🎯

Use the DataFrames imported in the first cell to explore the data and practice your skills!

  • Print the highest weekly sales for each department in the walmart DataFrame. Limit your results to the top five departments, in descending order. If you're stuck, try reviewing this video.
  • What was the total nb_sold of organic avocados in 2017 in the avocado DataFrame? If you're stuck, try reviewing this video.
  • Create a bar plot of the total number of homeless people by region in the homelessness DataFrame. Order the bars in descending order. Bonus: create a horizontal bar chart. If you're stuck, try reviewing this video.
  • Create a line plot with two lines representing the temperatures in Toronto and Rome. Make sure to properly label your plot. Bonus: add a legend for the two lines. If you're stuck, try reviewing this video.

Primer Problema 🤨

  • Agrupamos department con respecto a weekly_sales.
  • Sacamos las máximas ventas por semana.
  • Por último los ordenamos de forma descendente.
walmart.head()
Current Type: Bar
Current X-axis: department
Current Y-axis: weekly_sales
Current Color: None

Department vs weekly_sales

walmart.groupby('department')['weekly_sales'].agg(max).sort_values(ascending=False).head(5)

Segundo Problema 🎇

  • Se tomo la columna de nd_sold.
  • Luego se identifico en la columna year los mayores o iguales a 2017.
  • Al final sumamas los resultado de la columna nd_sold.
  • Guardamos el resultado en total_avocado
avocado.head()
total_avodaco = avocado[['nb_sold']].loc[avocado['year']>=2017].sum()
total_avodaco

Tercer Problema 🥳

  • Agrupamos region con respecto a family_members.
  • Obtenemos el total con sum().
  • Ordenamos de forma descendiente.
  • Creamos un gráfica de barras con plt.barh()
homelessness.head()
total_n_homeless = homelessness.groupby('region')['family_members'].agg(sum).sort_values(ascending=False)
plt.barh(total_n_homeless.index, total_n_homeless.values, align='center')
‌
‌
‌