😁 빅데이터 문제 풀기 & Study/- 클론코딩하기

[pandas] 🍒모두를 위한 데이터사이언스 클론코딩하기-3.수치형변수

또방91 2022. 1. 27. 21:59

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수치형 변수들을 잘 익혀두면 나중에 좋은 EDA를 할 수 있지않을까?란 생각에 코드를 써본당😉

In [3]:

from IPython.core.display import display, HTML 
display(HTML("<style>.container { width:100% !important; }</style>"))
#티스토리 업로드 원활하게:-)

🍒모두를 위한 데이터사이언스 클론코딩하기-3🍒¶

Pandas 공부하기

라이브러리 로드¶

In [4]:

import pandas as pd
import seaborn as sns

In [5]:

pd.__version__

Out[5]:

'1.3.4'

In [6]:

sns.__version__

Out[6]:

'0.11.2'

데이터셋 불러오기¶

In [7]:

#자동차 연비 데이터셋 불러오기
df = sns.load_dataset("mpg")

In [8]:

df

Out[8]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130.0	3504	12.0	70	usa	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693	11.5	70	usa	buick skylark 320
2	18.0	8	318.0	150.0	3436	11.0	70	usa	plymouth satellite
3	16.0	8	304.0	150.0	3433	12.0	70	usa	amc rebel sst
4	17.0	8	302.0	140.0	3449	10.5	70	usa	ford torino
...	...	...	...	...	...	...	...	...	...
393	27.0	4	140.0	86.0	2790	15.6	82	usa	ford mustang gl
394	44.0	4	97.0	52.0	2130	24.6	82	europe	vw pickup
395	32.0	4	135.0	84.0	2295	11.6	82	usa	dodge rampage
396	28.0	4	120.0	79.0	2625	18.6	82	usa	ford ranger
397	31.0	4	119.0	82.0	2720	19.4	82	usa	chevy s-10

398 rows × 9 columns

In [7]:

# 398 행 9열
df.shape

Out[7]:

(398, 9)

복습하기¶

In [9]:

df.index

Out[9]:

RangeIndex(start=0, stop=398, step=1)

In [10]:

df.columns

Out[10]:

Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',
       'acceleration', 'model_year', 'origin', 'name'],
      dtype='object')

In [11]:

df.values

Out[11]:

array([[18.0, 8, 307.0, ..., 70, 'usa', 'chevrolet chevelle malibu'],
       [15.0, 8, 350.0, ..., 70, 'usa', 'buick skylark 320'],
       [18.0, 8, 318.0, ..., 70, 'usa', 'plymouth satellite'],
       ...,
       [32.0, 4, 135.0, ..., 82, 'usa', 'dodge rampage'],
       [28.0, 4, 120.0, ..., 82, 'usa', 'ford ranger'],
       [31.0, 4, 119.0, ..., 82, 'usa', 'chevy s-10']], dtype=object)

In [12]:

df.dtypes

Out[12]:

mpg             float64
cylinders         int64
displacement    float64
horsepower      float64
weight            int64
acceleration    float64
model_year        int64
origin           object
name             object
dtype: object

In [13]:

# 위 5개 = 기본값
df.head()

Out[13]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130.0	3504	12.0	70	usa	chevrolet chevelle malibu
1	15.0	8	350.0	165.0	3693	11.5	70	usa	buick skylark 320
2	18.0	8	318.0	150.0	3436	11.0	70	usa	plymouth satellite
3	16.0	8	304.0	150.0	3433	12.0	70	usa	amc rebel sst
4	17.0	8	302.0	140.0	3449	10.5	70	usa	ford torino

In [14]:

#아래 5개 = 기본값
df.tail()

Out[14]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
393	27.0	4	140.0	86.0	2790	15.6	82	usa	ford mustang gl
394	44.0	4	97.0	52.0	2130	24.6	82	europe	vw pickup
395	32.0	4	135.0	84.0	2295	11.6	82	usa	dodge rampage
396	28.0	4	120.0	79.0	2625	18.6	82	usa	ford ranger
397	31.0	4	119.0	82.0	2720	19.4	82	usa	chevy s-10

In [15]:

#랜덤으로 1개 가져오기 = 기본값
df.sample()

Out[15]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
129	31.0	4	79.0	67.0	1950	19.0	74	japan	datsun b210

In [16]:

#비교
#랜덤으로 4개 가져오기 -> 실행할떄마다 같은 값
df.sample(4,random_state=42)

Out[16]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
198	33.0	4	91.0	53.0	1795	17.4	76	japan	honda civic
396	28.0	4	120.0	79.0	2625	18.6	82	usa	ford ranger
33	19.0	6	232.0	100.0	2634	13.0	71	usa	amc gremlin
208	13.0	8	318.0	150.0	3940	13.2	76	usa	plymouth volare premier v8

In [17]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   mpg           398 non-null    float64
 1   cylinders     398 non-null    int64  
 2   displacement  398 non-null    float64
 3   horsepower    392 non-null    float64
 4   weight        398 non-null    int64  
 5   acceleration  398 non-null    float64
 6   model_year    398 non-null    int64  
 7   origin        398 non-null    object 
 8   name          398 non-null    object 
dtypes: float64(4), int64(3), object(2)
memory usage: 28.1+ KB

In [19]:

#결측치수 확인하기
df.isnull().sum()

Out[19]:

mpg             0
cylinders       0
displacement    0
horsepower      6
weight          0
acceleration    0
model_year      0
origin          0
name            0
dtype: int64

In [20]:

#전체 값대비 결측치개수인 결측치 비율 확인
df.isnull().mean()

Out[20]:

mpg             0.000000
cylinders       0.000000
displacement    0.000000
horsepower      0.015075
weight          0.000000
acceleration    0.000000
model_year      0.000000
origin          0.000000
name            0.000000
dtype: float64

In [26]:

df.isnull().mean()*100

Out[26]:

mpg             0.000000
cylinders       0.000000
displacement    0.000000
horsepower      1.507538
weight          0.000000
acceleration    0.000000
model_year      0.000000
origin          0.000000
name            0.000000
dtype: float64

In [23]:

df.describe()

Out[23]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year
count	398.000000	398.000000	398.000000	392.000000	398.000000	398.000000	398.000000
mean	23.514573	5.454774	193.425879	104.469388	2970.424623	15.568090	76.010050
std	7.815984	1.701004	104.269838	38.491160	846.841774	2.757689	3.697627
min	9.000000	3.000000	68.000000	46.000000	1613.000000	8.000000	70.000000
25%	17.500000	4.000000	104.250000	75.000000	2223.750000	13.825000	73.000000
50%	23.000000	4.000000	148.500000	93.500000	2803.500000	15.500000	76.000000
75%	29.000000	8.000000	262.000000	126.000000	3608.000000	17.175000	79.000000
max	46.600000	8.000000	455.000000	230.000000	5140.000000	24.800000	82.000000

In [24]:

df.describe(include="object")

Out[24]:

	origin	name
count	398	398
unique	3	305
top	usa	ford pinto
freq	249	6

In [26]:

df["mpg"].unique()

Out[26]:

array([18. , 15. , 16. , 17. , 14. , 24. , 22. , 21. , 27. , 26. , 25. ,
       10. , 11. ,  9. , 28. , 19. , 12. , 13. , 23. , 30. , 31. , 35. ,
       20. , 29. , 32. , 33. , 17.5, 15.5, 14.5, 22.5, 24.5, 18.5, 29.5,
       26.5, 16.5, 31.5, 36. , 25.5, 33.5, 20.5, 30.5, 21.5, 43.1, 36.1,
       32.8, 39.4, 19.9, 19.4, 20.2, 19.2, 25.1, 20.6, 20.8, 18.6, 18.1,
       17.7, 27.5, 27.2, 30.9, 21.1, 23.2, 23.8, 23.9, 20.3, 21.6, 16.2,
       19.8, 22.3, 17.6, 18.2, 16.9, 31.9, 34.1, 35.7, 27.4, 25.4, 34.2,
       34.5, 31.8, 37.3, 28.4, 28.8, 26.8, 41.5, 38.1, 32.1, 37.2, 26.4,
       24.3, 19.1, 34.3, 29.8, 31.3, 37. , 32.2, 46.6, 27.9, 40.8, 44.3,
       43.4, 36.4, 44.6, 40.9, 33.8, 32.7, 23.7, 23.6, 32.4, 26.6, 25.8,
       23.5, 39.1, 39. , 35.1, 32.3, 37.7, 34.7, 34.4, 29.9, 33.7, 32.9,
       31.6, 28.1, 30.7, 24.2, 22.4, 34. , 38. , 44. ])

In [27]:

df["cylinders"].unique()

Out[27]:

array([8, 4, 6, 3, 5], dtype=int64)

In [29]:

df["mpg"].nunique()

Out[29]:

In [30]:

df.hist()

Out[30]:

array([[<AxesSubplot:title={'center':'mpg'}>,
        <AxesSubplot:title={'center':'cylinders'}>,
        <AxesSubplot:title={'center':'displacement'}>],
       [<AxesSubplot:title={'center':'horsepower'}>,
        <AxesSubplot:title={'center':'weight'}>,
        <AxesSubplot:title={'center':'acceleration'}>],
       [<AxesSubplot:title={'center':'model_year'}>, <AxesSubplot:>,
        <AxesSubplot:>]], dtype=object)

In [31]:

_ = df.hist()

In [32]:

_=df.hist(figsize=(10,10), bins=1)

In [33]:

_=df.hist(figsize=(8,8),bins=5)

In [35]:

_=df.hist(figsize=(8,8),bins=50)

비대칭도(왜도)¶

In [41]:

df.skew()

C:\Users\light\AppData\Local\Temp/ipykernel_5976/1665899112.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  df.skew()

Out[41]:

mpg             0.457066
cylinders       0.526922
displacement    0.719645
horsepower      1.087326
weight          0.531063
acceleration    0.278777
model_year      0.011535
dtype: float64

첨도¶

In [38]:

df.kurt()

C:\Users\light\AppData\Local\Temp/ipykernel_5976/1257127604.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  df.kurt()

Out[38]:

mpg            -0.510781
cylinders      -1.376662
displacement   -0.746597
horsepower      0.696947
weight         -0.785529
acceleration    0.419497
model_year     -1.181232
dtype: float64

1개의 수치 변수¶

In [42]:

sns.displot(data=df, x="mpg")

Out[42]:

<seaborn.axisgrid.FacetGrid at 0x1f145b415b0>

In [44]:

sns.displot(data=df, x="mpg", kde=True)

Out[44]:

<seaborn.axisgrid.FacetGrid at 0x1f14426a940>

In [45]:

sns.displot(data=df, x="mpg", kde=True, rug=True) #빈도수

Out[45]:

<seaborn.axisgrid.FacetGrid at 0x1f1470709d0>

In [47]:

sns.kdeplot(data=df["mpg"])

Out[47]:

<AxesSubplot:xlabel='mpg', ylabel='Density'>

In [49]:

#동일식
sns.kdeplot(data=df, x="mpg")

Out[49]:

<AxesSubplot:xlabel='mpg', ylabel='Density'>

In [50]:

sns.rugplot(data=df["mpg"])

Out[50]:

<AxesSubplot:xlabel='mpg'>

In [55]:

sns.kdeplot(data=df["mpg"], shade=True, cut=20) #cut으로 범위 바꾸기

Out[55]:

<AxesSubplot:xlabel='mpg', ylabel='Density'>

In [53]:

sns.kdeplot(data=df["mpg"], shade=True)

Out[53]:

<AxesSubplot:xlabel='mpg', ylabel='Density'>

In [58]:

df["mpg"].agg(["skew","kurt"])

# 왜도 왼쪽 오른쪽
# 첨도 뾰쪽한 정도

Out[58]:

skew    0.457066
kurt   -0.510781
Name: mpg, dtype: float64

In [59]:

sns.boxplot(data=df['mpg'])

Out[59]:

<AxesSubplot:>

In [64]:

sns.boxplot(data=df, x="mpg")

Out[64]:

<AxesSubplot:xlabel='mpg'>

In [67]:

df["mpg"].describe()

Out[67]:

count    398.000000
mean      23.514573
std        7.815984
min        9.000000
25%       17.500000
50%       23.000000
75%       29.000000
max       46.600000
Name: mpg, dtype: float64

In [69]:

sns.violinplot(data=df, x="mpg")

Out[69]:

<AxesSubplot:xlabel='mpg'>

In [71]:

sns.boxplot(data=df)

Out[71]:

<AxesSubplot:>

In [72]:

sns.violinplot(data=df)

Out[72]:

<AxesSubplot:>

2개이상 수치 변수¶

In [73]:

sns.scatterplot(data=df)

Out[73]:

<AxesSubplot:>

In [74]:

sns.scatterplot(data=df, x="weight", y="mpg")

Out[74]:

<AxesSubplot:xlabel='weight', ylabel='mpg'>

In [75]:

sns.scatterplot(data=df, x="weight", y="mpg", hue="origin")

Out[75]:

<AxesSubplot:xlabel='weight', ylabel='mpg'>

In [77]:

sns.regplot(data=df, x="weight", y="mpg") #회귀시각화

Out[77]:

<AxesSubplot:xlabel='weight', ylabel='mpg'>

In [78]:

sns.regplot(data=df, x="weight", y="mpg", scatter=False)

Out[78]:

<AxesSubplot:xlabel='weight', ylabel='mpg'>

In [79]:

sns.residplot(data=df, x="weight", y="mpg")

Out[79]:

<AxesSubplot:xlabel='weight', ylabel='mpg'>

In [80]:

sns.residplot(data=df, x="weight", y="mpg")

Out[80]:

<AxesSubplot:xlabel='weight', ylabel='mpg'>

In [84]:

sns.lmplot(data=df, x="weight", y="mpg")
# 동일식: sns.regplot(data=df, x="weight", y="mpg")

Out[84]:

<seaborn.axisgrid.FacetGrid at 0x1f146dfb7c0>

In [85]:

sns.lmplot(data=df, x="weight", y="mpg", hue="origin")

Out[85]:

<seaborn.axisgrid.FacetGrid at 0x1f142057a30>

In [86]:

sns.lmplot(data=df, x="weight", y="mpg", hue="origin", col="origin")

Out[86]:

<seaborn.axisgrid.FacetGrid at 0x1f1441494c0>

In [87]:

sns.lmplot(data=df, x="weight", y="mpg", hue="origin", col="origin", truncate=False)

Out[87]:

<seaborn.axisgrid.FacetGrid at 0x1f14741d8b0>

In [88]:

sns.jointplot(data=df, x="weight", y="mpg")

Out[88]:

<seaborn.axisgrid.JointGrid at 0x1f142e582b0>

In [89]:

sns.jointplot(data=df, x="weight", y="mpg", kind="kde")

Out[89]:

<seaborn.axisgrid.JointGrid at 0x1f1473d3a30>

In [91]:

sns.jointplot(data=df, x="weight", y="mpg", kind="reg")

Out[91]:

<seaborn.axisgrid.JointGrid at 0x1f1475aec10>

In [92]:

sns.jointplot(data=df, x="weight", y="mpg", kind="hex")

Out[92]:

<seaborn.axisgrid.JointGrid at 0x1f149618e80>

In [93]:

df_sample=df.sample(100)
df_sample

Out[93]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
85	13.0	8	350.0	175.0	4100	13.0	73	usa	buick century 350
172	25.0	4	90.0	71.0	2223	16.5	75	europe	volkswagen dasher
0	18.0	8	307.0	130.0	3504	12.0	70	usa	chevrolet chevelle malibu
209	19.0	4	120.0	88.0	3270	21.9	76	europe	peugeot 504
248	36.1	4	91.0	60.0	1800	16.4	78	japan	honda civic cvcc
...	...	...	...	...	...	...	...	...	...
241	22.0	6	146.0	97.0	2815	14.5	77	japan	datsun 810
194	22.5	6	232.0	90.0	3085	17.6	76	usa	amc hornet
78	21.0	4	120.0	87.0	2979	19.5	72	europe	peugeot 504 (sw)
121	15.0	8	318.0	150.0	3399	11.0	73	usa	dodge dart custom
56	26.0	4	91.0	70.0	1955	20.5	71	usa	plymouth cricket

100 rows × 9 columns

In [94]:

sns.pairplot(data=df_sample, hue="origin")

Out[94]:

<seaborn.axisgrid.PairGrid at 0x1f1498be520>

In [95]:

sns.scatterplot(data=df,x="model_year", y="mpg" )

Out[95]:

<AxesSubplot:xlabel='model_year', ylabel='mpg'>

In [96]:

sns.lineplot(data=df, x="model_year", y="mpg")

Out[96]:

<AxesSubplot:xlabel='model_year', ylabel='mpg'>

In [98]:

sns.lineplot(data=df, x="model_year", y="mpg", ci=None)
#ci는 신뢰구간을 의미

Out[98]:

<AxesSubplot:xlabel='model_year', ylabel='mpg'>

In [101]:

sns.relplot(data=df, x="model_year", y="mpg", hue="origin", col="origin" )

Out[101]:

<seaborn.axisgrid.FacetGrid at 0x1f14c546cd0>

In [102]:

sns.relplot(data=df)

Out[102]:

<seaborn.axisgrid.FacetGrid at 0x1f14c6ee430>

In [103]:

sns.relplot(data=df, x="model_year", y="mpg", col="origin", hue="origin", kind="line", ci=None)

Out[103]:

<seaborn.axisgrid.FacetGrid at 0x1f14c9bb3d0>

In [104]:

corr=df.corr()
corr

Out[104]:

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year
mpg	1.000000	-0.775396	-0.804203	-0.778427	-0.831741	0.420289	0.579267
cylinders	-0.775396	1.000000	0.950721	0.842983	0.896017	-0.505419	-0.348746
displacement	-0.804203	0.950721	1.000000	0.897257	0.932824	-0.543684	-0.370164
horsepower	-0.778427	0.842983	0.897257	1.000000	0.864538	-0.689196	-0.416361
weight	-0.831741	0.896017	0.932824	0.864538	1.000000	-0.417457	-0.306564
acceleration	0.420289	-0.505419	-0.543684	-0.689196	-0.417457	1.000000	0.288137
model_year	0.579267	-0.348746	-0.370164	-0.416361	-0.306564	0.288137	1.000000

In [111]:

import numpy as np
import matplotlib.pyplot as plt

In [107]:

np.ones_like(corr)

Out[107]:

array([[1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1.]])

In [108]:

mask = np.triu(np.ones_like(corr)) #상 삼각행렬만들기
mask

Out[108]:

array([[1., 1., 1., 1., 1., 1., 1.],
       [0., 1., 1., 1., 1., 1., 1.],
       [0., 0., 1., 1., 1., 1., 1.],
       [0., 0., 0., 1., 1., 1., 1.],
       [0., 0., 0., 0., 1., 1., 1.],
       [0., 0., 0., 0., 0., 1., 1.],
       [0., 0., 0., 0., 0., 0., 1.]])

In [116]:

print(plt.colormaps())

['Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'crest', 'crest_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'flare', 'flare_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'mako', 'mako_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'rocket', 'rocket_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'turbo', 'turbo_r', 'twilight', 'twilight_r', 'twilight_shifted', 'twilight_shifted_r', 'viridis', 'viridis_r', 'vlag', 'vlag_r', 'winter', 'winter_r']

In [114]:

sns.heatmap(corr, annot=True, cmap="coolwarm", vmax=1, vmin=-1)

Out[114]:

<AxesSubplot:>

In [120]:

sns.heatmap(corr, annot=True, cmap="bone", vmax=1, vmin=-1)

Out[120]:

<AxesSubplot:>

In [121]:

sns.heatmap(corr, annot=True, cmap="coolwarm", vmax=1, vmin=-1,mask=mask)

Out[121]:

<AxesSubplot:>

In [ ]:

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데이터셋 불러오기¶

복습하기¶

비대칭도(왜도)¶

첨도¶

1개의 수치 변수¶

2개이상 수치 변수¶

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