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Pandas for cycle in group by

I have a data set that has a category field, "City" and 2 metrics, age and weight. I want to build a scatter plot for each city using a cycle. However, I am struggling to combine the group and cycle that I need in a single statement. If I just use the for loop, I get a chart for each record, and if I make a group, I get the right number of charts, but without values.

Here is my code, using only the for loop with my group, commenting out:

import pandas as pd
import numpy as np
import matplotlib.pylab as plt


d = {  'City': pd.Series(['London','New York', 'New York', 'London', 'Paris',
                        'Paris','New York', 'New York', 'London','Paris']),
       'Age' : pd.Series([36., 42., 6., 66., 38.,18.,22.,43.,34.,54]),
     'Weight': pd.Series([225,454,345,355,234,198,400, 256,323,310])
}

df = pd.DataFrame(d)

#for C in df.groupby('City'):
for C in df.City:
    fig = plt.figure(figsize=(5, 4))
    # Create an Axes object.
    ax = fig.add_subplot(1,1,1) # one row, one column, first plot
    # Plot the data.
    ax.scatter(df.Age,df.Weight, df.City == C, color="red", marker="^")
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2 answers

Do not call plt.figuremore than once, as each call creates a new shape (roughly speaking, a window).

import pandas as pd
import numpy as np
import matplotlib.pylab as plt

d = {'City': ['London', 'New York', 'New York', 'London', 'Paris',
                        'Paris', 'New York', 'New York', 'London', 'Paris'],
     'Age': [36., 42., 6., 66., 38., 18., 22., 43., 34., 54],
     'Weight': [225, 454, 345, 355, 234, 198, 400, 256, 323, 310]}

df = pd.DataFrame(d)
fig, ax = plt.subplots(figsize=(5, 4))    # 1
df.groupby(['City']).plot(kind='scatter', x='Age', y='Weight', 
                          ax=ax,          # 2
                          color=['red', 'blue', 'green'])

plt.show()

enter image description here

  • plt.subplots fig , ax.
  • ax=ax Panda plot, .

:

import pandas as pd
import numpy as np
import matplotlib.pylab as plt

d = {'City': ['London', 'New York', 'New York', 'London', 'Paris',
                        'Paris', 'New York', 'New York', 'London', 'Paris'],
     'Age': [36., 42., 6., 66., 38., 18., 22., 43., 34., 54],
     'Weight': [225, 454, 345, 355, 234, 198, 400, 256, 323, 310]}

df = pd.DataFrame(d)
groups = df.groupby(['City'])
for city, grp in groups:                           # 1
    fig, ax = plt.subplots(figsize=(5, 4))
    grp.plot(kind='scatter', x='Age', y='Weight',  # 2
             ax=ax)               

    plt.show()
  • , , . GroupBy 2-: groupby -DataFrame.
  • grp, -DataFrame df for.
+2

, :

import pandas as pd
import numpy as np
import matplotlib.pylab as plt


d = {  'City': pd.Series(['London','New York', 'New York', 'London','Paris',
                        'Paris','New York', 'New York', 'London','Paris']),
       'Age' : pd.Series([36., 42., 6., 66., 38.,18.,22.,43.,34.,54]) ,
     'Weight': pd.Series([225,454,345,355,234,198,400, 256,323,310])

}

df = pd.DataFrame(d)

groups = df.groupby(['City'])
for city, grp in groups: 
    fig = plt.figure(figsize=(5, 4))
    # Create an Axes object.
    ax = fig.add_subplot(1,1,1) # one row, one column, first plot
    # Plot the data.
    ax.scatter(df.Age,df.Weight, df.City == city, color="red", marker="^")
+2

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