5. Visualizing DataFrames

1. Histograms

• Adjust the number of bars, or bins, using the “bins” argument. Increasing or decreasing the bins give better idea of what the distribution looks like

import matplotlib.pyplot as plt

dog_pack[“height_cm”].hist(bins=5)

plt.show()

• Example:

import matplotlib.pyplot as plt

avocados[avocados["type"] == "conventional"]["avg_price"].hist(alpha=0.5, bins=20)

avocados[avocados["type"] == "organic"]["avg_price"].hist(alpha=0.5, bins=20)

plt.legend(["conventional", "organic"]) # Add a legend

plt.show()

import matplotlib.pyplot as plt

print(df['Existing Zoning Sqft'].describe()) # Describe the column

df['Existing Zoning Sqft'].plot(kind='hist', rot=70, logx=True, logy=True) # Plot the histogram

plt.show()

2. Bar plots

• Bar plots can reveal relationships between a categorical variable and a numerical variable
• Create a bar plot from the mean weights using the plot method, setting “kind” equal to “bar”

import matplotlib.pyplot as plt

avg_weight_by_breed = dog_pack.groupby(“breed”)[“weight_kg”].mean()

avg_weight_by_breed.plot(kind = “bar”, title = “Mean Weight by Dog Breed”)

plt.show()

• Example:

import matplotlib.pyplot as plt # Import matplotlib.pyplot with alias plt

print(avocados.head())

nb_sold_by_size = avocados.groupby("size")["nb_sold"].sum() # Get the total number of avocados sold of each size

nb_sold_by_size.plot(kind="bar") # Create a bar plot of the number of avocados sold by size

plt.show()

3. Line plots

• Line plots are great for visualizing changes in numeric variables over time

import matplotlib.pyplot as plt

sully.plot(x = “date”, y = “weight_kg”, kind = “line”)

plt.show()

• Example:

import matplotlib.pyplot as plt # Import matplotlib.pyplot with alias plt

nb_sold_by_date = avocados.groupby("date")["nb_sold"].sum() # Get the total number of avocados sold on each date

nb_sold_by_date.plot(kind="line") # Create a line plot of the number of avocados sold by date

plt.show()

• Rotating axis labels

import matplotlib.pyplot as plt

sully.plot(x = “date”, y = “weight_kg”, kind = “line”, rot = 45) # By passing an angle in degrees with the “rot” argument

plt.show()

4. Scatter plots

• Scatter plots are great for visualizing relationships between two numeric variables

import matplotlib.pyplot as plt

dog_pack.plot(x = “height_cm”, y = “weight_kg”, kind = “scatter”)

plt.show()

• Example:

avocados.plot(x="nb_sold", y="avg_price", kind="scatter", title="Number of avocados sold vs. average price") # nb_sold vs avg_price

plt.show()