Cara menggunakan DF.PLOT.SCATTER pada Python

All examples can be viewed in this sample Jupyter notebook

Table of Contents Show

• Sample data for examples
• Scatter plot of two columns
• Bar plot of column values
• Line plot, multiple columns
• Save plot to file
• Bar plot with group by
• Stacked bar plot with group by
• Stacked bar plot with group by, normalized to 100%
• Stacked bar plot, two-level group by
• Stacked bar plot with two-level group by, normalized to 100%
• How do you plot a line graph from a DataFrame in Python?
• How do you create a line graph from a DataFrame?
• How do you do a line plot in Pandas DataFrame?
• How do you plot a Line in Python?

Table of Contents

• Sample data for examples
• Scatter plot of two columns
• Bar plot of column values
• Line plot, multiple columns
• Save plot to file
• Bar plot with group by
• Stacked bar plot with group by
• Stacked bar plot with group by, normalized to 100%
• Stacked bar plot, two-level group by
• Stacked bar plot with two-level group by, normalized to 100%
• How do you plot a line graph from a DataFrame in Python?
• How do you create a line graph from a DataFrame?
• How do you do a line plot in Pandas DataFrame?
• How do you plot a Line in Python?

You need to have the matplotlib module installed for this!

Versions used: Pandas 1.x, matplotlib 3.0.x

Sample data for examples

import pandas as pd

df = pd.DataFrame({
    'name':['john','mary','peter','jeff','bill','lisa','jose'],
    'age':[23,78,22,19,45,33,20],
    'gender':['M','F','M','M','M','F','M'],
    'state':['california','dc','california','dc','california','texas','texas'],
    'num_children':[2,0,0,3,2,1,4],
    'num_pets':[5,1,0,5,2,2,3]
})

Pandas has tight integration with matplotlib.

You can plot data directly from your DataFrame using the plot() method:

Scatter plot of two columns

import matplotlib.pyplot as plt
import pandas as pd

# a scatter plot comparing num_children and num_pets
df.plot(kind='scatter',x='num_children',y='num_pets',color='red')
plt.show()

Bar plot of column values

import matplotlib.pyplot as plt
import pandas as pd

# a simple line plot
df.plot(kind='bar',x='name',y='age')

Line plot, multiple columns

Just reuse the Axes object.

import matplotlib.pyplot as plt
import pandas as pd

# gca stands for 'get current axis'
ax = plt.gca()

df.plot(kind='line',x='name',y='num_children',ax=ax)
df.plot(kind='line',x='name',y='num_pets', color='red', ax=ax)

plt.show()

Save plot to file

Instead of calling plt.show(), call plt.savefig('outputfile.png'):

import matplotlib.pyplot as plt
import pandas as pd

df.plot(kind='bar',x='name',y='age')

# the plot gets saved to 'output.png'
plt.savefig('output.png')

Bar plot with group by

import matplotlib.pyplot as plt
import pandas as pd

df.groupby('state')['name'].nunique().plot(kind='bar')
plt.show()

Stacked bar plot with group by

Example: plot count by category as a stacked column:

• create a dummy variable and do a two-level group-by based on it:

• fix the x axis label and the legend

import matplotlib.pyplot as plt

# create dummy variable them group by that
# set the legend to false because we'll fix it later
df.assign(dummy = 1).groupby(
  ['dummy','state']
).size().to_frame().unstack().plot(kind='bar',stacked=True,legend=False)

plt.title('Number of records by State')

# other it'll show up as 'dummy' 
plt.xlabel('state')

# disable ticks in the x axis
plt.xticks([])

# fix the legend
current_handles, _ = plt.gca().get_legend_handles_labels()
reversed_handles = reversed(current_handles)

labels = reversed(df['state'].unique())

plt.legend(reversed_handles,labels,loc='lower right')
plt.show()

Stacked bar plot with group by, normalized to 100%

A plot where the columns sum up to 100%.

Similar to the example above but:

• normalize the values by dividing by the total amounts

• use percentage tick labels for the y axis

Example: Plot percentage count of records by state

import matplotlib.pyplot as plt
import matplotlib.ticker as mtick

# create dummy variable then group by that
# set the legend to false because we'll fix it later
df.assign(dummy = 1).groupby(
  ['dummy','state']
).size().groupby(level=0).apply(
    lambda x: 100 * x / x.sum()
).to_frame().unstack().plot(kind='bar',stacked=True,legend=False)

# or it'll show up as 'dummy' 
plt.xlabel('state')

# disable ticks in the x axis
plt.xticks([])

# fix the legend or it'll include the dummy variable
current_handles, _ = plt.gca().get_legend_handles_labels()
reversed_handles = reversed(current_handles)
correct_labels = reversed(df['state'].unique())

plt.legend(reversed_handles,correct_labels)

plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())
plt.show()

Stacked bar plot, two-level group by

Just do a normal groupby() and call unstack():

import matplotlib.pyplot as plt
import pandas as pd

df.groupby(['state','gender']).size().unstack().plot(kind='bar',stacked=True)
plt.show()

Another example: count the people by gender, spliting by state:

import matplotlib.pyplot as plt
import pandas as pd

df.groupby(['gender','state']).size().unstack().plot(kind='bar',stacked=True)
plt.show()

Stacked bar plot with two-level group by, normalized to 100%

Sometimes you are only ever interested in the distributions, not raw amounts:

import matplotlib.ticker as mtick
import matplotlib.pyplot as plt

df.groupby(['gender','state']).size().groupby(level=0).apply(
    lambda x: 100 * x / x.sum()
).unstack().plot(kind='bar',stacked=True)

plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())
plt.show()