데이터 분석 기초
기술통계, 시각화(6.8)
세용용용용
2023. 6. 8. 17:03
실습 파일들
drive-download-20230608T080436Z-001.zip
0.13MB
In [1]:
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
엑셀 파일 불러오기¶
In [2]:
import pandas as pd
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/남북한발전전력량.xlsx', header=None)
df
Out[2]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 전력량 (억㎾h) | 발전 전력별 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | ... | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
| 1 | 남한 | 합계 | 1077 | 1186 | 1310 | 1444 | 1650 | 1847 | 2055 | 2244 | ... | 4969 | 5096 | 5171 | 5220 | 5281 | 5404 | 5535 | 5707 | 5608 | 5450 |
| 2 | NaN | 수력 | 64 | 51 | 49 | 60 | 41 | 55 | 52 | 54 | ... | 78 | 77 | 84 | 78 | 58 | 66 | 70 | 73 | 62 | 71 |
| 3 | NaN | 화력 | 484 | 573 | 696 | 803 | 1022 | 1122 | 1264 | 1420 | ... | 3343 | 3430 | 3581 | 3427 | 3402 | 3523 | 3738 | 4018 | 3774 | 3466 |
| 4 | NaN | 원자력 | 529 | 563 | 565 | 581 | 587 | 670 | 739 | 771 | ... | 1547 | 1503 | 1388 | 1564 | 1648 | 1620 | 1484 | 1335 | 1459 | 1602 |
| 5 | NaN | 신재생 | - | - | - | - | - | - | - | - | ... | - | 86 | 118 | 151 | 173 | 195 | 243 | 281 | 313 | 311 |
| 6 | 북한 | 합계 | 277 | 263 | 247 | 221 | 231 | 230 | 213 | 193 | ... | 211 | 215 | 221 | 216 | 190 | 239 | 235 | 249 | 238 | 182 |
| 7 | NaN | 수력 | 156 | 150 | 142 | 133 | 138 | 142 | 125 | 107 | ... | 132 | 135 | 139 | 130 | 100 | 128 | 119 | 128 | 110 | 128 |
| 8 | NaN | 화력 | 121 | 113 | 105 | 88 | 93 | 88 | 88 | 86 | ... | 79 | 80 | 82 | 86 | 90 | 111 | 116 | 121 | 128 | 53.8 |
| 9 | NaN | 원자력 | - | - | - | - | - | - | - | - | ... | - | - | - | - | - | - | - | - | - | - |
10 rows × 33 columns
json 파일 불러오기¶
In [3]:
df = pd.read_json('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/read_json_sample.json')
df
Out[3]:
| name | year | developer | opensource | |
|---|---|---|---|---|
| pandas | 2008 | Wes Mckinneye | True | |
| NumPy | 2006 | Travis Oliphant | True | |
| matplotlib | 2003 | John D. Hunter | True |
csv파일로 저장하기¶
In [4]:
data = {
'name' : ['c0', 'c1', 'c2'],
'number1' : [0, 1, 2],
'number2' : [3, 4, 5],
'number3' : [6, 7, 8],
}
df = pd.DataFrame(data)
df.set_index('name', inplace=True)
display(df)
df.to_csv('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/df_csv_sample.csv')
| number1 | number2 | number3 | |
|---|---|---|---|
| name | |||
| c0 | 0 | 3 | 6 |
| c1 | 1 | 4 | 7 |
| c2 | 2 | 5 | 8 |
컬럼 설정하기¶
In [5]:
import pandas as pd
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/auto-mpg.xlsx', header = None)
df.columns = ['mpg', 'cylinders', 'displacement', 'hourespower',
'weight', 'acceleration', 'model_year', 'origin', 'name']
df
Out[5]:
| mpg | cylinders | displacement | hourespower | weight | acceleration | model_year | origin | name | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 18.0 | 8 | 307.0 | 130 | 3504 | 12.0 | 70 | 1 | chevrolet chevelle malibu |
| 1 | 15.0 | 8 | 350.0 | 165 | 3693 | 11.5 | 70 | 1 | buick skylark 320 |
| 2 | 18.0 | 8 | 318.0 | 150 | 3436 | 11.0 | 70 | 1 | plymouth satellite |
| 3 | 16.0 | 8 | 304.0 | 150 | 3433 | 12.0 | 70 | 1 | amc rebel sst |
| 4 | 17.0 | 8 | 302.0 | 140 | 3449 | 10.5 | 70 | 1 | ford torino |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 393 | 27.0 | 4 | 140.0 | 86 | 2790 | 15.6 | 82 | 1 | ford mustang gl |
| 394 | 44.0 | 4 | 97.0 | 52 | 2130 | 24.6 | 82 | 2 | vw pickup |
| 395 | 32.0 | 4 | 135.0 | 84 | 2295 | 11.6 | 82 | 1 | dodge rampage |
| 396 | 28.0 | 4 | 120.0 | 79 | 2625 | 18.6 | 82 | 1 | ford ranger |
| 397 | 31.0 | 4 | 119.0 | 82 | 2720 | 19.4 | 82 | 1 | chevy s-10 |
398 rows × 9 columns
데이터 미리보기¶
In [6]:
display('df.head()', df.head())
display('df.tail()', df.tail())
'df.head()'
| mpg | cylinders | displacement | hourespower | weight | acceleration | model_year | origin | name | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 18.0 | 8 | 307.0 | 130 | 3504 | 12.0 | 70 | 1 | chevrolet chevelle malibu |
| 1 | 15.0 | 8 | 350.0 | 165 | 3693 | 11.5 | 70 | 1 | buick skylark 320 |
| 2 | 18.0 | 8 | 318.0 | 150 | 3436 | 11.0 | 70 | 1 | plymouth satellite |
| 3 | 16.0 | 8 | 304.0 | 150 | 3433 | 12.0 | 70 | 1 | amc rebel sst |
| 4 | 17.0 | 8 | 302.0 | 140 | 3449 | 10.5 | 70 | 1 | ford torino |
'df.tail()'
| mpg | cylinders | displacement | hourespower | weight | acceleration | model_year | origin | name | |
|---|---|---|---|---|---|---|---|---|---|
| 393 | 27.0 | 4 | 140.0 | 86 | 2790 | 15.6 | 82 | 1 | ford mustang gl |
| 394 | 44.0 | 4 | 97.0 | 52 | 2130 | 24.6 | 82 | 2 | vw pickup |
| 395 | 32.0 | 4 | 135.0 | 84 | 2295 | 11.6 | 82 | 1 | dodge rampage |
| 396 | 28.0 | 4 | 120.0 | 79 | 2625 | 18.6 | 82 | 1 | ford ranger |
| 397 | 31.0 | 4 | 119.0 | 82 | 2720 | 19.4 | 82 | 1 | chevy s-10 |
데이터프레임 크기 확인¶
In [7]:
df.shape
Out[7]:
(398, 9)
데이터프레임 정보 확인¶
In [8]:
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 hourespower 398 non-null object 4 weight 398 non-null int64 5 acceleration 398 non-null float64 6 model_year 398 non-null int64 7 origin 398 non-null int64 8 name 398 non-null object dtypes: float64(3), int64(4), object(2) memory usage: 28.1+ KB
In [9]:
df.dtypes
Out[9]:
mpg float64 cylinders int64 displacement float64 hourespower object weight int64 acceleration float64 model_year int64 origin int64 name object dtype: object
컬럼들 기술통계 요약 정보¶
In [10]:
df.describe()
Out[10]:
| mpg | cylinders | displacement | weight | acceleration | model_year | origin | |
|---|---|---|---|---|---|---|---|
| count | 398.000000 | 398.000000 | 398.000000 | 398.000000 | 398.000000 | 398.000000 | 398.000000 |
| mean | 23.514573 | 5.454774 | 193.425879 | 2970.424623 | 15.568090 | 76.010050 | 1.572864 |
| std | 7.815984 | 1.701004 | 104.269838 | 846.841774 | 2.757689 | 3.697627 | 0.802055 |
| min | 9.000000 | 3.000000 | 68.000000 | 1613.000000 | 8.000000 | 70.000000 | 1.000000 |
| 25% | 17.500000 | 4.000000 | 104.250000 | 2223.750000 | 13.825000 | 73.000000 | 1.000000 |
| 50% | 23.000000 | 4.000000 | 148.500000 | 2803.500000 | 15.500000 | 76.000000 | 1.000000 |
| 75% | 29.000000 | 8.000000 | 262.000000 | 3608.000000 | 17.175000 | 79.000000 | 2.000000 |
| max | 46.600000 | 8.000000 | 455.000000 | 5140.000000 | 24.800000 | 82.000000 | 3.000000 |
In [11]:
df.describe(include = 'all')
Out[11]:
| mpg | cylinders | displacement | hourespower | weight | acceleration | model_year | origin | name | |
|---|---|---|---|---|---|---|---|---|---|
| count | 398.000000 | 398.000000 | 398.000000 | 398.0 | 398.000000 | 398.000000 | 398.000000 | 398.000000 | 398 |
| unique | NaN | NaN | NaN | 94.0 | NaN | NaN | NaN | NaN | 305 |
| top | NaN | NaN | NaN | 150.0 | NaN | NaN | NaN | NaN | ford pinto |
| freq | NaN | NaN | NaN | 22.0 | NaN | NaN | NaN | NaN | 6 |
| mean | 23.514573 | 5.454774 | 193.425879 | NaN | 2970.424623 | 15.568090 | 76.010050 | 1.572864 | NaN |
| std | 7.815984 | 1.701004 | 104.269838 | NaN | 846.841774 | 2.757689 | 3.697627 | 0.802055 | NaN |
| min | 9.000000 | 3.000000 | 68.000000 | NaN | 1613.000000 | 8.000000 | 70.000000 | 1.000000 | NaN |
| 25% | 17.500000 | 4.000000 | 104.250000 | NaN | 2223.750000 | 13.825000 | 73.000000 | 1.000000 | NaN |
| 50% | 23.000000 | 4.000000 | 148.500000 | NaN | 2803.500000 | 15.500000 | 76.000000 | 1.000000 | NaN |
| 75% | 29.000000 | 8.000000 | 262.000000 | NaN | 3608.000000 | 17.175000 | 79.000000 | 2.000000 | NaN |
| max | 46.600000 | 8.000000 | 455.000000 | NaN | 5140.000000 | 24.800000 | 82.000000 | 3.000000 | NaN |
데이터의 개수 뽑아오기¶
In [12]:
df.count()
Out[12]:
mpg 398 cylinders 398 displacement 398 hourespower 398 weight 398 acceleration 398 model_year 398 origin 398 name 398 dtype: int64
특정 열의 데이터 개수 출출¶
In [13]:
df['origin'].value_counts()
Out[13]:
1 249 3 79 2 70 Name: origin, dtype: int64
In [14]:
df.mean()
<ipython-input-14-c61f0c8f89b5>:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. df.mean()
Out[14]:
mpg 23.514573 cylinders 5.454774 displacement 193.425879 weight 2970.424623 acceleration 15.568090 model_year 76.010050 origin 1.572864 dtype: float64
In [15]:
df[['mpg', 'displacement']].mean()
Out[15]:
mpg 23.514573 displacement 193.425879 dtype: float64
중간값¶
In [16]:
df.median()
<ipython-input-16-6d467abf240d>:1: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. df.median()
Out[16]:
mpg 23.0 cylinders 4.0 displacement 148.5 weight 2803.5 acceleration 15.5 model_year 76.0 origin 1.0 dtype: float64
In [17]:
df[['mpg', 'displacement']].median()
Out[17]:
mpg 23.0 displacement 148.5 dtype: float64
최댓값¶
In [18]:
df.max()
<ipython-input-18-4c1ddf8920ff>:1: FutureWarning: The default value of numeric_only in DataFrame.max is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. df.max()
Out[18]:
mpg 46.6 cylinders 8 displacement 455.0 weight 5140 acceleration 24.8 model_year 82 origin 3 name vw rabbit custom dtype: object
In [19]:
df[['mpg', 'displacement']].max()
Out[19]:
mpg 46.6 displacement 455.0 dtype: float64
최솟값¶
In [20]:
df.min()
<ipython-input-20-c3612c624a3f>:1: FutureWarning: The default value of numeric_only in DataFrame.min is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. df.min()
Out[20]:
mpg 9.0 cylinders 3 displacement 68.0 weight 1613 acceleration 8.0 model_year 70 origin 1 name amc ambassador brougham dtype: object
In [21]:
df[['mpg', 'displacement']].min()
Out[21]:
mpg 9.0 displacement 68.0 dtype: float64
표준편차¶
In [22]:
df.std()
<ipython-input-22-ce97bb7eaef8>:1: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. df.std()
Out[22]:
mpg 7.815984 cylinders 1.701004 displacement 104.269838 weight 846.841774 acceleration 2.757689 model_year 3.697627 origin 0.802055 dtype: float64
In [23]:
df[['mpg', 'displacement']].std()
Out[23]:
mpg 7.815984 displacement 104.269838 dtype: float64
분산¶
In [24]:
df.var()
<ipython-input-24-28ded241fd7c>:1: FutureWarning: The default value of numeric_only in DataFrame.var is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning. df.var()
Out[24]:
mpg 61.089611 cylinders 2.893415 displacement 10872.199152 weight 717140.990526 acceleration 7.604848 model_year 13.672443 origin 0.643292 dtype: float64
In [25]:
df[['mpg', 'displacement']].var()
Out[25]:
mpg 61.089611 displacement 10872.199152 dtype: float64
In [26]:
df.corr()
<ipython-input-26-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. df.corr()
Out[26]:
| mpg | cylinders | displacement | weight | acceleration | model_year | origin | |
|---|---|---|---|---|---|---|---|
| mpg | 1.000000 | -0.775396 | -0.804203 | -0.831741 | 0.420289 | 0.579267 | 0.563450 |
| cylinders | -0.775396 | 1.000000 | 0.950721 | 0.896017 | -0.505419 | -0.348746 | -0.562543 |
| displacement | -0.804203 | 0.950721 | 1.000000 | 0.932824 | -0.543684 | -0.370164 | -0.609409 |
| weight | -0.831741 | 0.896017 | 0.932824 | 1.000000 | -0.417457 | -0.306564 | -0.581024 |
| acceleration | 0.420289 | -0.505419 | -0.543684 | -0.417457 | 1.000000 | 0.288137 | 0.205873 |
| model_year | 0.579267 | -0.348746 | -0.370164 | -0.306564 | 0.288137 | 1.000000 | 0.180662 |
| origin | 0.563450 | -0.562543 | -0.609409 | -0.581024 | 0.205873 | 0.180662 | 1.000000 |
In [27]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# 상관관계 계산
corr_matrix = df.corr()
# 히트맵 그리기
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()
<ipython-input-27-e60676ae1155>:6: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. corr_matrix = df.corr()
판다스 내장 그래프 확인¶
In [28]:
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/남북한발전전력량.xlsx')
df
Out[28]:
| 전력량 (억㎾h) | 발전 전력별 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | ... | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 남한 | 합계 | 1077 | 1186 | 1310 | 1444 | 1650 | 1847 | 2055 | 2244 | ... | 4969 | 5096 | 5171 | 5220 | 5281 | 5404 | 5535 | 5707 | 5608 | 5450 |
| 1 | NaN | 수력 | 64 | 51 | 49 | 60 | 41 | 55 | 52 | 54 | ... | 78 | 77 | 84 | 78 | 58 | 66 | 70 | 73 | 62 | 71 |
| 2 | NaN | 화력 | 484 | 573 | 696 | 803 | 1022 | 1122 | 1264 | 1420 | ... | 3343 | 3430 | 3581 | 3427 | 3402 | 3523 | 3738 | 4018 | 3774 | 3466 |
| 3 | NaN | 원자력 | 529 | 563 | 565 | 581 | 587 | 670 | 739 | 771 | ... | 1547 | 1503 | 1388 | 1564 | 1648 | 1620 | 1484 | 1335 | 1459 | 1602 |
| 4 | NaN | 신재생 | - | - | - | - | - | - | - | - | ... | - | 86 | 118 | 151 | 173 | 195 | 243 | 281 | 313 | 311 |
| 5 | 북한 | 합계 | 277 | 263 | 247 | 221 | 231 | 230 | 213 | 193 | ... | 211 | 215 | 221 | 216 | 190 | 239 | 235 | 249 | 238 | 182 |
| 6 | NaN | 수력 | 156 | 150 | 142 | 133 | 138 | 142 | 125 | 107 | ... | 132 | 135 | 139 | 130 | 100 | 128 | 119 | 128 | 110 | 128 |
| 7 | NaN | 화력 | 121 | 113 | 105 | 88 | 93 | 88 | 88 | 86 | ... | 79 | 80 | 82 | 86 | 90 | 111 | 116 | 121 | 128 | 53.8 |
| 8 | NaN | 원자력 | - | - | - | - | - | - | - | - | ... | - | - | - | - | - | - | - | - | - | - |
9 rows × 33 columns
In [29]:
df_ns = df.iloc[[0,5],2:]
df_ns.index = ['south','north']
df_ns.columns = df_ns.columns.map(int)
display(df_ns)
df_ns.plot()
| 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | ... | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| south | 1077 | 1186 | 1310 | 1444 | 1650 | 1847 | 2055 | 2244 | 2153 | 2393 | ... | 4969 | 5096 | 5171 | 5220 | 5281 | 5404 | 5535 | 5707 | 5608 | 5450 |
| north | 277 | 263 | 247 | 221 | 231 | 230 | 213 | 193 | 170 | 186 | ... | 211 | 215 | 221 | 216 | 190 | 239 | 235 | 249 | 238 | 182 |
2 rows × 31 columns
Out[29]:
<Axes: >
In [30]:
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/남북한발전전력량.xlsx')
df_ns = df.iloc[[0,5],2:]
df_ns.index = ['south','north']
df_ns.columns = df_ns.columns.map(int)
df_ns = df_ns.T
display(df_ns.head(5))
df_ns.plot()
| south | north | |
|---|---|---|
| 1990 | 1077 | 277 |
| 1991 | 1186 | 263 |
| 1992 | 1310 | 247 |
| 1993 | 1444 | 221 |
| 1994 | 1650 | 231 |
Out[30]:
<Axes: >
In [31]:
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/남북한발전전력량.xlsx')
df_ns = df.iloc[[0,5],2:]
df_ns.index = ['south','north']
df_ns.columns = df_ns.columns.map(int)
df_ns = df_ns.T
display(df_ns.head(5))
df_ns.plot(kind = 'bar')
| south | north | |
|---|---|---|
| 1990 | 1077 | 277 |
| 1991 | 1186 | 263 |
| 1992 | 1310 | 247 |
| 1993 | 1444 | 221 |
| 1994 | 1650 | 231 |
Out[31]:
<Axes: >
In [32]:
import pandas as pd
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/auto-mpg.xlsx', header = None)
df.columns = ['mpg', 'cylinders', 'displacement', 'hourespower',
'weight', 'acceleration', 'model_year', 'origin', 'name']
df.plot(x = 'mpg', y = 'weight', kind = 'scatter')
Out[32]:
<Axes: xlabel='mpg', ylabel='weight'>
In [33]:
import pandas as pd
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/auto-mpg.xlsx', header = None)
df.columns = ['mpg', 'cylinders', 'displacement', 'hourespower',
'weight', 'acceleration', 'model_year', 'origin', 'name']
df['mpg'].plot(kind='box')
Out[33]:
<Axes: >
matplotlib 시각화 도구¶
In [34]:
import pandas as pd
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/시도별 전출입 인구수.xlsx')
df.head()
Out[34]:
| 전출지별 | 전입지별 | 1970 | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | ... | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 전출지별 | 전입지별 | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | ... | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) |
| 1 | 전국 | 전국 | 4046536 | 4210164 | 3687938 | 4860418 | 5297969 | 9011440 | 6773250 | 7397623 | ... | 8808256 | 8487275 | 8226594 | 8127195 | 7506691 | 7411784 | 7629098 | 7755286 | 7378430 | 7154226 |
| 2 | NaN | 서울특별시 | 1742813 | 1671705 | 1349333 | 1831858 | 2050392 | 3396662 | 2756510 | 2893403 | ... | 2025358 | 1873188 | 1733015 | 1721748 | 1555281 | 1520090 | 1573594 | 1589431 | 1515602 | 1472937 |
| 3 | NaN | 부산광역시 | 448577 | 389797 | 362202 | 482061 | 680984 | 805979 | 724664 | 785117 | ... | 514502 | 519310 | 519334 | 508043 | 461042 | 478451 | 485710 | 507031 | 459015 | 439073 |
| 4 | NaN | 대구광역시 | - | - | - | - | - | - | - | - | ... | 409938 | 398626 | 370817 | 370563 | 348642 | 351873 | 350213 | 351424 | 328228 | 321182 |
5 rows × 50 columns
In [35]:
df = df.fillna(method='ffill')
df
Out[35]:
| 전출지별 | 전입지별 | 1970 | 1971 | 1972 | 1973 | 1974 | 1975 | 1976 | 1977 | ... | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 전출지별 | 전입지별 | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | ... | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) | 이동자수 (명) |
| 1 | 전국 | 전국 | 4046536 | 4210164 | 3687938 | 4860418 | 5297969 | 9011440 | 6773250 | 7397623 | ... | 8808256 | 8487275 | 8226594 | 8127195 | 7506691 | 7411784 | 7629098 | 7755286 | 7378430 | 7154226 |
| 2 | 전국 | 서울특별시 | 1742813 | 1671705 | 1349333 | 1831858 | 2050392 | 3396662 | 2756510 | 2893403 | ... | 2025358 | 1873188 | 1733015 | 1721748 | 1555281 | 1520090 | 1573594 | 1589431 | 1515602 | 1472937 |
| 3 | 전국 | 부산광역시 | 448577 | 389797 | 362202 | 482061 | 680984 | 805979 | 724664 | 785117 | ... | 514502 | 519310 | 519334 | 508043 | 461042 | 478451 | 485710 | 507031 | 459015 | 439073 |
| 4 | 전국 | 대구광역시 | - | - | - | - | - | - | - | - | ... | 409938 | 398626 | 370817 | 370563 | 348642 | 351873 | 350213 | 351424 | 328228 | 321182 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 320 | 제주특별자치도 | 전라북도 | 139 | 202 | 141 | 210 | 219 | 415 | 392 | 408 | ... | 668 | 579 | 672 | 561 | 551 | 516 | 609 | 683 | 733 | 768 |
| 321 | 제주특별자치도 | 전라남도 | 631 | 965 | 857 | 952 | 1133 | 2808 | 2608 | 2652 | ... | 1143 | 1123 | 1002 | 1026 | 966 | 1001 | 928 | 1062 | 1127 | 1102 |
| 322 | 제주특별자치도 | 경상북도 | 374 | 619 | 468 | 576 | 625 | 1123 | 1141 | 1004 | ... | 761 | 704 | 738 | 756 | 699 | 781 | 728 | 903 | 931 | 994 |
| 323 | 제주특별자치도 | 경상남도 | 474 | 479 | 440 | 571 | 1208 | 1517 | 863 | 1122 | ... | 1517 | 1474 | 1324 | 1367 | 1227 | 1278 | 1223 | 1500 | 1448 | 1501 |
| 324 | 제주특별자치도 | 제주특별자치도 | 9290 | 12427 | 12210 | 16158 | 19580 | 34221 | 23291 | 31028 | ... | 59564 | 55673 | 55507 | 59846 | 54280 | 60607 | 59673 | 59036 | 66444 | 63275 |
325 rows × 50 columns
서울특별시에서 다른 곳으로 이동한 인구 수 추이¶
In [36]:
#설치후 런타임 재시작
#!sudo apt-get install -y fonts-nanum
#!sudo fc-cache -fv
#!rm ~/.cache/matplotlib -rf
In [41]:
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'NanumBarunGothic'
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/시도별 전출입 인구수.xlsx')
df = df.fillna(method = "ffill")
mask = (df['전출지별'] == "서울특별시") & (df['전입지별'] != "서울특별시")
df_seoul = df[mask]
df_seoul.drop(['전출지별'], axis=1, inplace = True)
df_seoul.rename({'전입지별':'전입지'}, axis=1, inplace=True)
df_seoul.set_index('전입지', inplace=True)
sr_gg = df_seoul.loc['경기도']
# 그림판 크기 확장
plt.figure(figsize = (14, 5))
plt.plot(sr_gg.index, sr_gg.values)
# x축의 눈금 이름 기울이기
plt.xticks(rotation = 90)
# plt.xticks(rotation = "vertical")
plt.title("서울 -> 경기 이동 인구수")
plt.xlabel("기간")
plt.ylabel("이동 인구수")
plt.show()
<ipython-input-41-261a7f2f8e73>:12: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_seoul.drop(['전출지별'], axis=1, inplace = True)
<ipython-input-41-261a7f2f8e73>:13: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_seoul.rename({'전입지별':'전입지'}, axis=1, inplace=True)
In [43]:
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'NanumBarunGothic'
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/시도별 전출입 인구수.xlsx')
df = df.fillna(method = "ffill")
mask = (df['전출지별'] == "서울특별시") & (df['전입지별'] != "서울특별시")
df_seoul = df[mask]
df_seoul.drop(['전출지별'], axis=1, inplace = True)
df_seoul.rename({'전입지별':'전입지'}, axis=1, inplace=True)
df_seoul.set_index('전입지', inplace=True)
sr_gg = df_seoul.loc['경기도']
# 그림판 테마 바꾸기
plt.style.use('ggplot')
plt.figure(figsize = (14, 5))
# 차트를 그릴 때 여러 속성 넣기 가능
plt.plot(sr_gg.index, sr_gg.values, marker = "o", markersize = 10)
plt.xticks(rotation = 90)
plt.title("서울 -> 경기 이동 인구수")
plt.xlabel("기간")
plt.ylabel("이동 인구수")
plt.show()
<ipython-input-43-c5af1814a28a>:12: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_seoul.drop(['전출지별'], axis=1, inplace = True)
<ipython-input-43-c5af1814a28a>:13: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_seoul.rename({'전입지별':'전입지'}, axis=1, inplace=True)
In [44]:
import matplotlib.pyplot as plt
print(plt.style.available)
['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']
In [45]:
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.family'] = 'NanumBarunGothic'
df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/2023코랩/csv모음/시도별 전출입 인구수.xlsx')
df = df.fillna(method = "ffill")
mask = (df['전출지별'] == "서울특별시") & (df['전입지별'] != "서울특별시")
df_seoul = df[mask]
df_seoul.drop(['전출지별'], axis=1, inplace = True)
df_seoul.rename({'전입지별':'전입지'}, axis=1, inplace=True)
df_seoul.set_index('전입지', inplace=True)
sr_gg = df_seoul.loc['경기도']
plt.style.use('ggplot')
plt.figure(figsize = (14, 5))
plt.plot(sr_gg.index, sr_gg.values, marker = "o", markersize = 10)
# y축 값의 범위 지정 가능
plt.ylim(50000, 800000)
# 화살표 표시
plt.annotate('', # 텍스트
xy=(20, 550000), # 머리 부분
xytext=(3, 350000), # 꼬리 부분
xycoords='data', # 좌표 체계
arrowprops=dict(arrowstyle='->', color='skyblue', lw=5)) # 화살표 특성
plt.annotate('인구 이동 증가', # 텍스트
xy=(10, 470000), # 텍스트 기준
rotation = 21, # 기울임(시계 반대 방향)
va="center", # 수직(위 / 아래) - (top / bottom)
ha="center", # 수평(좌 / 우) - (left / right)
fontsize=15) # 글자 크기
# 화살표 표시
plt.annotate('', # 텍스트
xy=(45, 430000), # 머리 부분
xytext=(34, 560000), # 꼬리 부분
xycoords='data', # 좌표 체계
arrowprops=dict(arrowstyle='->', color='skyblue', lw=5)) # 화살표 특성
plt.annotate('인구 이동 감소', # 텍스트
xy=(40, 520000), # 텍스트 기준
rotation = -21, # 기울임(시계 반대 방향)
va="center", # 수직(위 / 아래) - (top / bottom)
ha="center", # 수평(좌 / 우) - (left / right)
fontsize=15) # 글자 크기
plt.xticks(rotation = 90)
plt.title("서울 -> 경기 이동 인구수")
plt.xlabel("기간")
plt.ylabel("이동 인구수")
plt.show()
<ipython-input-45-f269fce5f4b0>:12: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_seoul.drop(['전출지별'], axis=1, inplace = True)
<ipython-input-45-f269fce5f4b0>:13: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_seoul.rename({'전입지별':'전입지'}, axis=1, inplace=True)
