Python : Pandas Visualization

pandas의 plot은 내부적으로 matplotlib.pyplot을 이용한다.

 

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

df1 = pd.DataFrame(np.random.randn(100, 3), 
                  index=pd.date_range('1/1/2019', periods=100),
                  columns=['A', 'B', 'C']).cumsum()

df1

	A	B	C
2019-01-01	-0.896370	-1.962732	1.584821
2019-01-02	-0.248402	-3.101740	0.370419
2019-01-03	0.622560	-3.979711	1.666569
2019-01-04	1.239019	-3.443114	2.071264
2019-01-05	1.430470	-2.562603	1.617184
...	...	...	...
2019-04-06	-3.766329	1.028045	-0.010892
2019-04-07	-1.085759	0.827237	-1.009741
2019-04-08	-1.825895	0.261739	-0.533710
2019-04-09	-3.983964	1.580290	-0.773006
2019-04-10	-4.230757	0.500947	-0.887232

100 rows × 3 columns

df1.plot()
plt.title('Pandas Plot Example')
plt.xlabel('Dates')
plt.ylabel('Data')
plt.show()

import seaborn as sns

titanic = sns.load_dataset('titanic')

titanic

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

891 rows × 15 columns

iris = sns.load_dataset('iris')

iris

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	virginica
146	6.3	2.5	5.0	1.9	virginica
147	6.5	3.0	5.2	2.0	virginica
148	6.2	3.4	5.4	2.3	virginica
149	5.9	3.0	5.1	1.8	virginica

150 rows × 5 columns

iris.sepal_length[:20].plot(kind='line', rot=0)

<matplotlib.axes._subplots.AxesSubplot at 0x1a25717a10>

iris.sepal_length[:20].plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x1a245dcfd0>

df2 = iris.groupby(iris.species).mean()

df2

	sepal_length	sepal_width	petal_length	petal_width
species
setosa	5.006	3.428	1.462	0.246
versicolor	5.936	2.770	4.260	1.326
virginica	6.588	2.974	5.552	2.026

df2.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x1a2460be50>

df2.T.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x1a2470c490>

df3 = titanic.pclass.value_counts()
df3

3    491
1    216
2    184
Name: pclass, dtype: int64

df3.plot.pie(autopct='%.2f%%')
plt.axis('equal')

(-1.110415878418142, 1.100496015606113, -1.134350102435046, 1.112420061514539)

iris.plot.hist()

<matplotlib.axes._subplots.AxesSubplot at 0x1a24bea850>

iris.plot.kde()

<matplotlib.axes._subplots.AxesSubplot at 0x1a24d43050>

iris.plot.box()

<matplotlib.axes._subplots.AxesSubplot at 0x1a24e3cb10>

 

iris.boxplot(by='species')

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a24d25590>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24c16b90>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a24c49dd0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24acbd90>]],
      dtype=object)