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raintrooper
Asked: 2020-05-27 21:14:57 +0800 CST 2020-05-27 21:14:57 +0800 CST

How do I generate a plot with Matplotlib from a dataframe?

  • 772

I am trying to generate a plot with matplotlib from a dataframe. What I want to show on the graph is a correlation between the age of people and the money they earn and spend. The problem is that I can't make the graph understandable or readable.

I tried to group the information so that, for example, the ages and their respective values ​​are not repeated, but I have not succeeded.

If you could help me to correctly generate the graph please. Thank you

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

import matplotlib.pyplot as plt
import pandas as pd

data = [{'Genre':1,'Age':19,'annualincome':15,'annualexpenses':39},
{'Genre':1,'Age':21,'annualincome':15,'annualexpenses':81},
{'Genre':0,'Age':20,'annualincome':16,'annualexpenses':6},
{'Genre':0,'Age':23,'annualincome':16,'annualexpenses':77},
{'Genre':0,'Age':31,'annualincome':17,'annualexpenses':40},
{'Genre':0,'Age':22,'annualincome':17,'annualexpenses':76},
{'Genre':0,'Age':35,'annualincome':18,'annualexpenses':6},
{'Genre':0,'Age':23,'annualincome':18,'annualexpenses':94},
{'Genre':1,'Age':64,'annualincome':19,'annualexpenses':3},
{'Genre':0,'Age':30,'annualincome':19,'annualexpenses':72},
{'Genre':1,'Age':67,'annualincome':19,'annualexpenses':14},
{'Genre':0,'Age':35,'annualincome':19,'annualexpenses':99},
{'Genre':0,'Age':58,'annualincome':20,'annualexpenses':15},
{'Genre':0,'Age':24,'annualincome':20,'annualexpenses':77},
{'Genre':1,'Age':37,'annualincome':20,'annualexpenses':13},
{'Genre':1,'Age':22,'annualincome':20,'annualexpenses':79},
{'Genre':0,'Age':35,'annualincome':21,'annualexpenses':35},
{'Genre':1,'Age':20,'annualincome':21,'annualexpenses':66},
{'Genre':1,'Age':52,'annualincome':23,'annualexpenses':29},
{'Genre':0,'Age':35,'annualincome':23,'annualexpenses':98},
{'Genre':1,'Age':35,'annualincome':24,'annualexpenses':35},
{'Genre':1,'Age':25,'annualincome':24,'annualexpenses':73},
{'Genre':0,'Age':46,'annualincome':25,'annualexpenses':5},
{'Genre':1,'Age':31,'annualincome':25,'annualexpenses':73},
{'Genre':0,'Age':54,'annualincome':28,'annualexpenses':14},
{'Genre':1,'Age':29,'annualincome':28,'annualexpenses':82},
{'Genre':0,'Age':45,'annualincome':28,'annualexpenses':32},
{'Genre':1,'Age':35,'annualincome':28,'annualexpenses':61},
{'Genre':0,'Age':40,'annualincome':29,'annualexpenses':31},
{'Genre':0,'Age':23,'annualincome':29,'annualexpenses':87},
{'Genre':1,'Age':60,'annualincome':30,'annualexpenses':4},
{'Genre':0,'Age':21,'annualincome':30,'annualexpenses':73},
{'Genre':1,'Age':53,'annualincome':33,'annualexpenses':4},
{'Genre':1,'Age':18,'annualincome':33,'annualexpenses':92},
{'Genre':0,'Age':49,'annualincome':33,'annualexpenses':14},
{'Genre':0,'Age':21,'annualincome':33,'annualexpenses':81},
{'Genre':0,'Age':42,'annualincome':34,'annualexpenses':17},
{'Genre':0,'Age':30,'annualincome':34,'annualexpenses':73},
{'Genre':0,'Age':36,'annualincome':37,'annualexpenses':26},
{'Genre':0,'Age':20,'annualincome':37,'annualexpenses':75},
{'Genre':0,'Age':65,'annualincome':38,'annualexpenses':35},
{'Genre':1,'Age':24,'annualincome':38,'annualexpenses':92},
{'Genre':1,'Age':48,'annualincome':39,'annualexpenses':36},
{'Genre':0,'Age':31,'annualincome':39,'annualexpenses':61},
{'Genre':0,'Age':49,'annualincome':39,'annualexpenses':28},
{'Genre':0,'Age':24,'annualincome':39,'annualexpenses':65},
{'Genre':0,'Age':50,'annualincome':40,'annualexpenses':55},
{'Genre':0,'Age':27,'annualincome':40,'annualexpenses':47},
{'Genre':0,'Age':29,'annualincome':40,'annualexpenses':42},
{'Genre':0,'Age':31,'annualincome':40,'annualexpenses':42},
{'Genre':0,'Age':49,'annualincome':42,'annualexpenses':52},
{'Genre':1,'Age':33,'annualincome':42,'annualexpenses':60},
{'Genre':0,'Age':31,'annualincome':43,'annualexpenses':54},
{'Genre':1,'Age':59,'annualincome':43,'annualexpenses':60},
{'Genre':0,'Age':50,'annualincome':43,'annualexpenses':45},
{'Genre':1,'Age':47,'annualincome':43,'annualexpenses':41},
{'Genre':0,'Age':51,'annualincome':44,'annualexpenses':50},
{'Genre':1,'Age':69,'annualincome':44,'annualexpenses':46},
{'Genre':0,'Age':27,'annualincome':46,'annualexpenses':51},
{'Genre':1,'Age':53,'annualincome':46,'annualexpenses':46},
{'Genre':1,'Age':70,'annualincome':46,'annualexpenses':56},
{'Genre':1,'Age':19,'annualincome':46,'annualexpenses':55},
{'Genre':0,'Age':67,'annualincome':47,'annualexpenses':52},
{'Genre':0,'Age':54,'annualincome':47,'annualexpenses':59},
{'Genre':1,'Age':63,'annualincome':48,'annualexpenses':51},
{'Genre':1,'Age':18,'annualincome':48,'annualexpenses':59},
{'Genre':0,'Age':43,'annualincome':48,'annualexpenses':50},
{'Genre':0,'Age':68,'annualincome':48,'annualexpenses':48},
{'Genre':1,'Age':19,'annualincome':48,'annualexpenses':59},
{'Genre':0,'Age':32,'annualincome':48,'annualexpenses':47},
{'Genre':1,'Age':70,'annualincome':49,'annualexpenses':55},
{'Genre':0,'Age':47,'annualincome':49,'annualexpenses':42},
{'Genre':0,'Age':60,'annualincome':50,'annualexpenses':49},
{'Genre':0,'Age':60,'annualincome':50,'annualexpenses':56},
{'Genre':1,'Age':59,'annualincome':54,'annualexpenses':47},
{'Genre':1,'Age':26,'annualincome':54,'annualexpenses':54},
{'Genre':0,'Age':45,'annualincome':54,'annualexpenses':53},
{'Genre':1,'Age':40,'annualincome':54,'annualexpenses':48},
{'Genre':0,'Age':23,'annualincome':54,'annualexpenses':52},
{'Genre':0,'Age':49,'annualincome':54,'annualexpenses':42},
{'Genre':1,'Age':57,'annualincome':54,'annualexpenses':51},
{'Genre':1,'Age':38,'annualincome':54,'annualexpenses':55},
{'Genre':1,'Age':67,'annualincome':54,'annualexpenses':41},
{'Genre':0,'Age':46,'annualincome':54,'annualexpenses':44},
{'Genre':0,'Age':21,'annualincome':54,'annualexpenses':57},
{'Genre':1,'Age':48,'annualincome':54,'annualexpenses':46},
{'Genre':0,'Age':55,'annualincome':57,'annualexpenses':58},
{'Genre':0,'Age':22,'annualincome':57,'annualexpenses':55},
{'Genre':0,'Age':34,'annualincome':58,'annualexpenses':60},
{'Genre':0,'Age':50,'annualincome':58,'annualexpenses':46},
{'Genre':0,'Age':68,'annualincome':59,'annualexpenses':55},
{'Genre':1,'Age':18,'annualincome':59,'annualexpenses':41},
{'Genre':1,'Age':48,'annualincome':60,'annualexpenses':49},
{'Genre':0,'Age':40,'annualincome':60,'annualexpenses':40},
{'Genre':0,'Age':32,'annualincome':60,'annualexpenses':42},
{'Genre':1,'Age':24,'annualincome':60,'annualexpenses':52},
{'Genre':0,'Age':47,'annualincome':60,'annualexpenses':47},
{'Genre':0,'Age':27,'annualincome':60,'annualexpenses':50},
{'Genre':1,'Age':48,'annualincome':61,'annualexpenses':42},
{'Genre':1,'Age':20,'annualincome':61,'annualexpenses':49},
{'Genre':0,'Age':23,'annualincome':62,'annualexpenses':41},
{'Genre':0,'Age':49,'annualincome':62,'annualexpenses':48},
{'Genre':1,'Age':67,'annualincome':62,'annualexpenses':59},
{'Genre':1,'Age':26,'annualincome':62,'annualexpenses':55},
{'Genre':1,'Age':49,'annualincome':62,'annualexpenses':56},
{'Genre':0,'Age':21,'annualincome':62,'annualexpenses':42},
{'Genre':0,'Age':66,'annualincome':63,'annualexpenses':50},
{'Genre':1,'Age':54,'annualincome':63,'annualexpenses':46},
{'Genre':1,'Age':68,'annualincome':63,'annualexpenses':43},
{'Genre':1,'Age':66,'annualincome':63,'annualexpenses':48},
{'Genre':1,'Age':65,'annualincome':63,'annualexpenses':52},
{'Genre':0,'Age':19,'annualincome':63,'annualexpenses':54},
{'Genre':0,'Age':38,'annualincome':64,'annualexpenses':42},
{'Genre':1,'Age':19,'annualincome':64,'annualexpenses':46},
{'Genre':0,'Age':18,'annualincome':65,'annualexpenses':48},
{'Genre':0,'Age':19,'annualincome':65,'annualexpenses':50},
{'Genre':0,'Age':63,'annualincome':65,'annualexpenses':43},
{'Genre':0,'Age':49,'annualincome':65,'annualexpenses':59},
{'Genre':0,'Age':51,'annualincome':67,'annualexpenses':43},
{'Genre':0,'Age':50,'annualincome':67,'annualexpenses':57},
{'Genre':1,'Age':27,'annualincome':67,'annualexpenses':56},
{'Genre':0,'Age':38,'annualincome':67,'annualexpenses':40},
{'Genre':0,'Age':40,'annualincome':69,'annualexpenses':58},
{'Genre':1,'Age':39,'annualincome':69,'annualexpenses':91},
{'Genre':0,'Age':23,'annualincome':70,'annualexpenses':29},
{'Genre':0,'Age':31,'annualincome':70,'annualexpenses':77},
{'Genre':1,'Age':43,'annualincome':71,'annualexpenses':35},
{'Genre':1,'Age':40,'annualincome':71,'annualexpenses':95},
{'Genre':1,'Age':59,'annualincome':71,'annualexpenses':11},
{'Genre':1,'Age':38,'annualincome':71,'annualexpenses':75},
{'Genre':1,'Age':47,'annualincome':71,'annualexpenses':9},
{'Genre':1,'Age':39,'annualincome':71,'annualexpenses':75},
{'Genre':0,'Age':25,'annualincome':72,'annualexpenses':34},
{'Genre':0,'Age':31,'annualincome':72,'annualexpenses':71},
{'Genre':1,'Age':20,'annualincome':73,'annualexpenses':5},
{'Genre':0,'Age':29,'annualincome':73,'annualexpenses':88},
{'Genre':0,'Age':44,'annualincome':73,'annualexpenses':7},
{'Genre':1,'Age':32,'annualincome':73,'annualexpenses':73},
{'Genre':1,'Age':19,'annualincome':74,'annualexpenses':10},
{'Genre':0,'Age':35,'annualincome':74,'annualexpenses':72},
{'Genre':0,'Age':57,'annualincome':75,'annualexpenses':5},
{'Genre':1,'Age':32,'annualincome':75,'annualexpenses':93},
{'Genre':0,'Age':28,'annualincome':76,'annualexpenses':40},
{'Genre':0,'Age':32,'annualincome':76,'annualexpenses':87},
{'Genre':1,'Age':25,'annualincome':77,'annualexpenses':12},
{'Genre':1,'Age':28,'annualincome':77,'annualexpenses':97},
{'Genre':1,'Age':48,'annualincome':77,'annualexpenses':36},
{'Genre':0,'Age':32,'annualincome':77,'annualexpenses':74},
{'Genre':0,'Age':34,'annualincome':78,'annualexpenses':22},
{'Genre':1,'Age':34,'annualincome':78,'annualexpenses':90},
{'Genre':1,'Age':43,'annualincome':78,'annualexpenses':17},
{'Genre':1,'Age':39,'annualincome':78,'annualexpenses':88},
{'Genre':0,'Age':44,'annualincome':78,'annualexpenses':20},
{'Genre':0,'Age':38,'annualincome':78,'annualexpenses':76},
{'Genre':0,'Age':47,'annualincome':78,'annualexpenses':16},
{'Genre':0,'Age':27,'annualincome':78,'annualexpenses':89},
{'Genre':1,'Age':37,'annualincome':78,'annualexpenses':1},
{'Genre':0,'Age':30,'annualincome':78,'annualexpenses':78},
{'Genre':1,'Age':34,'annualincome':78,'annualexpenses':1},
{'Genre':0,'Age':30,'annualincome':78,'annualexpenses':73},
{'Genre':0,'Age':56,'annualincome':79,'annualexpenses':35},
{'Genre':0,'Age':29,'annualincome':79,'annualexpenses':83},
{'Genre':1,'Age':19,'annualincome':81,'annualexpenses':5},
{'Genre':0,'Age':31,'annualincome':81,'annualexpenses':93},
{'Genre':1,'Age':50,'annualincome':85,'annualexpenses':26},
{'Genre':0,'Age':36,'annualincome':85,'annualexpenses':75},
{'Genre':1,'Age':42,'annualincome':86,'annualexpenses':20},
{'Genre':0,'Age':33,'annualincome':86,'annualexpenses':95},
{'Genre':0,'Age':36,'annualincome':87,'annualexpenses':27},
{'Genre':1,'Age':32,'annualincome':87,'annualexpenses':63},
{'Genre':1,'Age':40,'annualincome':87,'annualexpenses':13},
{'Genre':1,'Age':28,'annualincome':87,'annualexpenses':75},
{'Genre':1,'Age':36,'annualincome':87,'annualexpenses':10},
{'Genre':1,'Age':36,'annualincome':87,'annualexpenses':92},
{'Genre':0,'Age':52,'annualincome':88,'annualexpenses':13},
{'Genre':0,'Age':30,'annualincome':88,'annualexpenses':86},
{'Genre':1,'Age':58,'annualincome':88,'annualexpenses':15},
{'Genre':1,'Age':27,'annualincome':88,'annualexpenses':69},
{'Genre':1,'Age':59,'annualincome':93,'annualexpenses':14},
{'Genre':1,'Age':35,'annualincome':93,'annualexpenses':90},
{'Genre':0,'Age':37,'annualincome':97,'annualexpenses':32},
{'Genre':0,'Age':32,'annualincome':97,'annualexpenses':86},
{'Genre':1,'Age':46,'annualincome':98,'annualexpenses':15},
{'Genre':0,'Age':29,'annualincome':98,'annualexpenses':88},
{'Genre':0,'Age':41,'annualincome':99,'annualexpenses':39},
{'Genre':1,'Age':30,'annualincome':99,'annualexpenses':97},
{'Genre':0,'Age':54,'annualincome':101,'annualexpenses':24},
{'Genre':1,'Age':28,'annualincome':101,'annualexpenses':68},
{'Genre':0,'Age':41,'annualincome':103,'annualexpenses':17},
{'Genre':0,'Age':36,'annualincome':103,'annualexpenses':85},
{'Genre':0,'Age':34,'annualincome':103,'annualexpenses':23},
{'Genre':0,'Age':32,'annualincome':103,'annualexpenses':69},
{'Genre':1,'Age':33,'annualincome':113,'annualexpenses':8},
{'Genre':0,'Age':38,'annualincome':113,'annualexpenses':91},
{'Genre':0,'Age':47,'annualincome':120,'annualexpenses':16},
{'Genre':0,'Age':35,'annualincome':120,'annualexpenses':79},
{'Genre':0,'Age':45,'annualincome':126,'annualexpenses':28},
{'Genre':1,'Age':32,'annualincome':126,'annualexpenses':74},
{'Genre':1,'Age':32,'annualincome':137,'annualexpenses':18},
{'Genre':1,'Age':30,'annualincome':137,'annualexpenses':83}

]

df = pd.DataFrame.from_dict(data, orient='columns')

edades = df.Age
ingresos = df.annualincome
egresos = df.annualexpenses
x1 = np.arange(len(edades))  # the label locations
width = 0.35  # the width of the bars
fig, ax = plt.subplots()
rect1 = ax.bar(x1 - width/2, ingresos, width, label='annualincome')
rect2 = ax.bar(x1 + width/2, egresos, width, label='annualexpenses')
ax.set_ylabel('Scores')
ax.set_title('Scores by ingresos y egresos')
ax.set_xticks(x1)
ax.set_xticklabels(edades)
ax.legend()

def autolabel(rects):
    """Attach a text label above each bar in *rects*, displaying its height."""
    for rect in rects:
        height = rect.get_height()
        ax.annotate('{}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')


autolabel(rect1)
autolabel(rect2)

fig.tight_layout()

plt.show()

graph

python

2 Answers

  1. Best Answer

    To improve your visualization, the first step is to create a new dataframe grouping the average income and expenses by age, and then make the graph. In this way, the points are reduced and it facilitates the analysis. To apply these solutions I have started from your dataframe df:

    def my_aggs(x):
    names = {
        'MeanAnnualIncome': x['annualincome'].mean(),
        'MeanAnnuaExpenses':  x['annualexpenses'].mean()}
    return pd.Series(names, index=['MeanAnnualIncome','MeanAnnuaExpenses'])

df1=df.groupby(['Age']).apply(my_aggs).reset_index()
df1.head()

    enter image description here

    edad=df1.Age
ingresos = df1.MeanAnnualIncome
gastos = df1.MeanAnnuaExpenses

    Another aspect that I think can improve your visualization (by having many points), is to use another type of graph, eg lines instead of bars (you can choose the one that seems clearer to you).

    Visualization example 1: By age ranges

    plt.plot(edad, ingresos,label='Ingresos', linewidth=2)
plt.plot(edad,gastos,label='Gastos', linewidth=2)
plt.ylabel('Ingresos / Gastos')
plt.xlabel('Edad')
plt.legend()
plt.grid(True)

plt.show()

    By age ranges

    Visualization example 2: With all ages

    countEdades=list(range(min(edad),min(edad)+len(edad)+2))

fig = plt.figure()
rect = (0,0, 2, 2)
axes = fig.add_axes(rect)

axes.plot(edad, ingresos,label='Ingresos', linewidth=3)
axes.plot(edad, gastos,label='Gastos', linewidth=3)
axes.set_ylim(0, 100)
axes.set_xticks(countEdades)
axes.legend()
axes.grid(True)
axes.set_title('Ingresos y Gastos por edad')
axes.set_xlabel("edades")
axes.set_ylabel("Ingresos / Gastos")

    including all ages

    Changing display 2 to bars:

    fig = plt.figure()
rect = (0,0, 2, 2)
axes = fig.add_axes(rect)

axes.bar(edad, ingresos,label='Ingresos', linewidth=3)
axes.bar(edad, gastos,label='Gastos', linewidth=3)
axes.set_ylim(0, 100)
axes.set_xticks(countEdades)
axes.legend()
axes.grid(True)
axes.set_title('Ingresos y Gastos por edad')
axes.set_xlabel("edades")
axes.set_ylabel("Ingresos / Gastos")

    enter image description here

    Visualization example 3: Using Seaborn we can use a scatterplot with regression lines to represent the relationship between the age of people and the money they earn and spend . First of all, we change the shape of our dataframe df1, to join the average amounts in a single column and we create an additional column that indicates if the type of amount is Income or Expense:

    dfIngresos=df.groupby(['Age'])['annualincome'].agg(['mean']).reset_index()
dfIngresos['Type']='Ingreso'

dfGastos=df.groupby(['Age'])['annualexpenses'].agg(['mean']).reset_index()
dfGastos['Type']='Gasto'

df2=pd.concat([dfIngresos, dfGastos])
df2

    New form of the data in df2

    import seaborn as sns
x=df2.Age
y=df2.mean

sns.lmplot('Age', 'mean', hue='Type',data=df2, fit_reg=True)

    scatterplot with regression lines

    • 5

  2. to group test like this:

    import matplotlib
import matplotlib.pyplot as plt
import numpy as np

import matplotlib.pyplot as plt
import pandas as pd

data = [{'Genre':1,'Age':19,'annualincome':15,'annualexpenses':39},
{'Genre':1,'Age':21,'annualincome':15,'annualexpenses':81},
{'Genre':0,'Age':20,'annualincome':16,'annualexpenses':6},
{'Genre':0,'Age':23,'annualincome':16,'annualexpenses':77},
{'Genre':0,'Age':31,'annualincome':17,'annualexpenses':40},
{'Genre':0,'Age':22,'annualincome':17,'annualexpenses':76},
{'Genre':0,'Age':35,'annualincome':18,'annualexpenses':6},
{'Genre':0,'Age':23,'annualincome':18,'annualexpenses':94},
{'Genre':1,'Age':64,'annualincome':19,'annualexpenses':3},
{'Genre':0,'Age':30,'annualincome':19,'annualexpenses':72},
{'Genre':1,'Age':67,'annualincome':19,'annualexpenses':14},
{'Genre':0,'Age':35,'annualincome':19,'annualexpenses':99},
{'Genre':0,'Age':58,'annualincome':20,'annualexpenses':15},
{'Genre':0,'Age':24,'annualincome':20,'annualexpenses':77},
{'Genre':1,'Age':37,'annualincome':20,'annualexpenses':13},
{'Genre':1,'Age':22,'annualincome':20,'annualexpenses':79},
{'Genre':0,'Age':35,'annualincome':21,'annualexpenses':35},
{'Genre':1,'Age':20,'annualincome':21,'annualexpenses':66},
{'Genre':1,'Age':52,'annualincome':23,'annualexpenses':29},
{'Genre':0,'Age':35,'annualincome':23,'annualexpenses':98},
{'Genre':1,'Age':35,'annualincome':24,'annualexpenses':35},
{'Genre':1,'Age':25,'annualincome':24,'annualexpenses':73},
{'Genre':0,'Age':46,'annualincome':25,'annualexpenses':5},
{'Genre':1,'Age':31,'annualincome':25,'annualexpenses':73},
{'Genre':0,'Age':54,'annualincome':28,'annualexpenses':14},
{'Genre':1,'Age':29,'annualincome':28,'annualexpenses':82},
{'Genre':0,'Age':45,'annualincome':28,'annualexpenses':32},
{'Genre':1,'Age':35,'annualincome':28,'annualexpenses':61},

]

df = pd.DataFrame.from_dict(data, orient='columns')
grouped_gender = df.groupby("Genre")
grouped_gender.groups

for names, groups in grouped_gender:
    print(names)
    print(groups)



grouped_gender.aggregate(
    {
        "Age" : np.mean,
        "annualincome": np.sum,
        "annualexpenses" :np.std
    }
)
    • 0