7일차
08 그룹으로 묶어 연산하기¶
분할-적용-결합 => 분산 커퓨팅 분야에서 빅테이터를 처리하고자 사용했던 방법 그룹 연산을 사용하면 더 큰 데이터도 손쉽게 처리할 수 있으므로 반드시 알아야 합니다.
In [1]:
import pandas as pd
df = pd.read_csv('../data/gapminder.tsv', sep='\t')
In [2]:
avg_life_exp_by_year = df.groupby('year')["lifeExp"].mean()
print(avg_life_exp_by_year)
year 1952 49.057620 1957 51.507401 1962 53.609249 1967 55.678290 1972 57.647386 1977 59.570157 1982 61.533197 1987 63.212613 1992 64.160338 1997 65.014676 2002 65.694923 2007 67.007423 Name: lifeExp, dtype: float64
In [3]:
years = df.year.unique()
print(years)
[1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007]
In [4]:
y1952 = df.loc[df.year == 1952, :]
print(y1952)
country continent year lifeExp pop gdpPercap 0 Afghanistan Asia 1952 28.801 8425333 779.445314 12 Albania Europe 1952 55.230 1282697 1601.056136 24 Algeria Africa 1952 43.077 9279525 2449.008185 36 Angola Africa 1952 30.015 4232095 3520.610273 48 Argentina Americas 1952 62.485 17876956 5911.315053 ... ... ... ... ... ... ... 1644 Vietnam Asia 1952 40.412 26246839 605.066492 1656 West Bank and Gaza Asia 1952 43.160 1030585 1515.592329 1668 Yemen, Rep. Asia 1952 32.548 4963829 781.717576 1680 Zambia Africa 1952 42.038 2672000 1147.388831 1692 Zimbabwe Africa 1952 48.451 3080907 406.884115 [142 rows x 6 columns]
In [5]:
y1592_mean = y1952["lifeExp"].mean()
print(y1592_mean)
49.057619718309866
In [6]:
continent_describe = df.groupby('continent')["lifeExp"].describe()
print(continent_describe)
count mean std min 25% 50% 75% \
continent
Africa 624.0 48.865330 9.150210 23.599 42.37250 47.7920 54.41150
Americas 300.0 64.658737 9.345088 37.579 58.41000 67.0480 71.69950
Asia 396.0 60.064903 11.864532 28.801 51.42625 61.7915 69.50525
Europe 360.0 71.903686 5.433178 43.585 69.57000 72.2410 75.45050
Oceania 24.0 74.326208 3.795611 69.120 71.20500 73.6650 77.55250
max
continent
Africa 76.442
Americas 80.653
Asia 82.603
Europe 81.757
Oceania 81.235
In [7]:
import numpy as np
cont_le_agg = df.groupby('continent')["lifeExp"].agg(np.mean)
print(cont_le_agg)
continent Africa 48.865330 Americas 64.658737 Asia 60.064903 Europe 71.903686 Oceania 74.326208 Name: lifeExp, dtype: float64
In [8]:
def my_mean(values):
n = len(values)
sum = 0
for value in values:
sum += value
return sum / n
In [9]:
agg_my_mean = df.groupby('year')["lifeExp"].agg(my_mean)
print(agg_my_mean)
year 1952 49.057620 1957 51.507401 1962 53.609249 1967 55.678290 1972 57.647386 1977 59.570157 1982 61.533197 1987 63.212613 1992 64.160338 1997 65.014676 2002 65.694923 2007 67.007423 Name: lifeExp, dtype: float64
In [10]:
def my_mean_diff(values, diff_value):
n = len(values)
sum = 0
for value in values:
sum += value
mean = sum / n
return mean - diff_value
In [11]:
global_mean = df['lifeExp'].mean()
print(global_mean)
59.474439366197174
In [12]:
agg_mean_diff = (
df.groupby("year")["lifeExp"].agg(my_mean_diff, diff_value=global_mean)
)
print(agg_mean_diff)
year 1952 -10.416820 1957 -7.967038 1962 -5.865190 1967 -3.796150 1972 -1.827053 1977 0.095718 1982 2.058758 1987 3.738173 1992 4.685899 1997 5.540237 2002 6.220483 2007 7.532983 Name: lifeExp, dtype: float64
In [14]:
gdf = (
df.groupby("year")
["lifeExp"]
.agg([np.count_nonzero, np.mean, np.std])
)
print(gdf)
count_nonzero mean std year 1952 142 49.057620 12.225956 1957 142 51.507401 12.231286 1962 142 53.609249 12.097245 1967 142 55.678290 11.718858 1972 142 57.647386 11.381953 1977 142 59.570157 11.227229 1982 142 61.533197 10.770618 1987 142 63.212613 10.556285 1992 142 64.160338 11.227380 1997 142 65.014676 11.559439 2002 142 65.694923 12.279823 2007 142 67.007423 12.073021
In [15]:
gdf_dict = df.groupby("year").agg(
{
"lifeExp": "mean",
"pop": "median",
"gdpPercap": "median"
}
)
print(gdf_dict)
lifeExp pop gdpPercap year 1952 49.057620 3943953.0 1968.528344 1957 51.507401 4282942.0 2173.220291 1962 53.609249 4686039.5 2335.439533 1967 55.678290 5170175.5 2678.334740 1972 57.647386 5877996.5 3339.129407 1977 59.570157 6404036.5 3798.609244 1982 61.533197 7007320.0 4216.228428 1987 63.212613 7774861.5 4280.300366 1992 64.160338 8688686.5 4386.085502 1997 65.014676 9735063.5 4781.825478 2002 65.694923 10372918.5 5319.804524 2007 67.007423 10517531.0 6124.371108
In [16]:
gdf = (
df.groupby("year")
["lifeExp"]
.agg(
[
np.count_nonzero,
np.mean,
np.std,
]
)
.rename(
columns={
"count_nonzero": "count",
"mean": "avg",
"std": "std_dev",
}
)
.reset_index()
)
print(gdf)
year count avg std_dev 0 1952 142 49.057620 12.225956 1 1957 142 51.507401 12.231286 2 1962 142 53.609249 12.097245 3 1967 142 55.678290 11.718858 4 1972 142 57.647386 11.381953 5 1977 142 59.570157 11.227229 6 1982 142 61.533197 10.770618 7 1987 142 63.212613 10.556285 8 1992 142 64.160338 11.227380 9 1997 142 65.014676 11.559439 10 2002 142 65.694923 12.279823 11 2007 142 67.007423 12.073021
08-2 데이터 변환하기¶
표준점수: 평균과의 차이를 표준편차로 나눈 값을 표준점수라고 합니다.
In [17]:
def my_zscore(x):
return (x - x.mean()) / x.std()
In [18]:
transform_z = df.groupby('year')["lifeExp"].transform(my_zscore)
print(transform_z)
0 -1.656854
1 -1.731249
2 -1.786543
3 -1.848157
4 -1.894173
...
1699 -0.081621
1700 -0.336974
1701 -1.574962
1702 -2.093346
1703 -1.948180
Name: lifeExp, Length: 1704, dtype: float64
In [19]:
print(df.shape)
(1704, 6)
In [20]:
print(transform_z.shape)
(1704,)
In [21]:
from scipy.stats import zscore
In [22]:
sp_z_grouped = df.groupby('year')["lifeExp"].transform(zscore)
In [23]:
sp_z_nogroup = zscore(df["lifeExp"])
In [24]:
print(transform_z.head())
0 -1.656854 1 -1.731249 2 -1.786543 3 -1.848157 4 -1.894173 Name: lifeExp, dtype: float64
In [25]:
print(sp_z_grouped.head())
0 -1.662719 1 -1.737377 2 -1.792867 3 -1.854699 4 -1.900878 Name: lifeExp, dtype: float64
In [27]:
print(sp_z_nogroup[:5])
0 -2.375334 1 -2.256774 2 -2.127837 3 -1.971178 4 -1.811033 Name: lifeExp, dtype: float64
In [28]:
import seaborn as sns
np.random.seed(42)
tips_10 = sns.load_dataset("tips").sample(10)
In [29]:
tips_10.loc[
np.random.permutation(tips_10.index)[:4],
"total_bill"
] = np.NaN
print(tips_10.head())
total_bill tip sex smoker day time size 24 19.82 3.18 Male No Sat Dinner 2 6 8.77 2.00 Male No Sun Dinner 2 153 NaN 2.00 Male No Sun Dinner 4 211 NaN 5.16 Male Yes Sat Dinner 4 198 NaN 2.00 Female Yes Thur Lunch 2
In [30]:
count_sex = tips_10.groupby('sex').count()
print(count_sex)
total_bill tip smoker day time size sex Male 4 7 7 7 7 7 Female 2 3 3 3 3 3
In [31]:
def fill_na_mean(x):
avg = x.mean()
return x.fillna(avg)
total_bill_group_mean = (
tips_10
.groupby("sex")
.total_bill.transform(fill_na_mean)
)
In [32]:
tips_10['fill_total_bill'] = total_bill_group_mean
In [33]:
print(tips_10[['sex', 'total_bill', 'fill_total_bill']])
sex total_bill fill_total_bill 24 Male 19.82 19.8200 6 Male 8.77 8.7700 153 Male NaN 17.9525 211 Male NaN 17.9525 198 Female NaN 13.9300 176 Male NaN 17.9525 192 Male 28.44 28.4400 124 Female 12.48 12.4800 9 Male 14.78 14.7800 101 Female 15.38 15.3800
In [34]:
tips = sns.load_dataset("tips")
print(tips.shape)
(244, 7)
In [35]:
print(tips['size'].value_counts())
size 2 156 3 38 4 37 5 5 1 4 6 4 Name: count, dtype: int64
In [36]:
tips_filtered = (
tips.groupby("size")
.filter(lambda x: x["size"].count() >= 30)
)
In [37]:
print(tips_filtered.shape)
(231, 7)
In [38]:
print(tips_filtered['size'].value_counts())
size 2 156 3 38 4 37 Name: count, dtype: int64
In [39]:
tips_10 = sns.load_dataset("tips").sample(10, random_state=42)
print(tips_10)
total_bill tip sex smoker day time size 24 19.82 3.18 Male No Sat Dinner 2 6 8.77 2.00 Male No Sun Dinner 2 153 24.55 2.00 Male No Sun Dinner 4 211 25.89 5.16 Male Yes Sat Dinner 4 198 13.00 2.00 Female Yes Thur Lunch 2 176 17.89 2.00 Male Yes Sun Dinner 2 192 28.44 2.56 Male Yes Thur Lunch 2 124 12.48 2.52 Female No Thur Lunch 2 9 14.78 3.23 Male No Sun Dinner 2 101 15.38 3.00 Female Yes Fri Dinner 2
In [40]:
grouped = tips_10.groupby('sex')
print(grouped)
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000013D578647D0>
In [41]:
print(grouped.groups)
{'Male': [24, 6, 153, 211, 176, 192, 9], 'Female': [198, 124, 101]}
In [42]:
avgs = grouped.mean(numeric_only=True)
print(avgs)
total_bill tip size sex Male 20.02 2.875714 2.571429 Female 13.62 2.506667 2.000000
In [43]:
print(tips_10.columns)
Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')
In [44]:
female = grouped.get_group('Female')
print(female)
total_bill tip sex smoker day time size 198 13.00 2.00 Female Yes Thur Lunch 2 124 12.48 2.52 Female No Thur Lunch 2 101 15.38 3.00 Female Yes Fri Dinner 2
In [45]:
for sex_group in grouped:
print(sex_group)
('Male', total_bill tip sex smoker day time size
24 19.82 3.18 Male No Sat Dinner 2
6 8.77 2.00 Male No Sun Dinner 2
153 24.55 2.00 Male No Sun Dinner 4
211 25.89 5.16 Male Yes Sat Dinner 4
176 17.89 2.00 Male Yes Sun Dinner 2
192 28.44 2.56 Male Yes Thur Lunch 2
9 14.78 3.23 Male No Sun Dinner 2)
('Female', total_bill tip sex smoker day time size
198 13.00 2.00 Female Yes Thur Lunch 2
124 12.48 2.52 Female No Thur Lunch 2
101 15.38 3.00 Female Yes Fri Dinner 2)
In [46]:
print(grouped[0])
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) Cell In[46], line 1 ----> 1 print(grouped[0]) File ~\anaconda3\Lib\site-packages\pandas\core\groupby\generic.py:1771, in DataFrameGroupBy.__getitem__(self, key) 1764 if isinstance(key, tuple) and len(key) > 1: 1765 # if len == 1, then it becomes a SeriesGroupBy and this is actually 1766 # valid syntax, so don't raise 1767 raise ValueError( 1768 "Cannot subset columns with a tuple with more than one element. " 1769 "Use a list instead." 1770 ) -> 1771 return super().__getitem__(key) File ~\anaconda3\Lib\site-packages\pandas\core\base.py:244, in SelectionMixin.__getitem__(self, key) 242 else: 243 if key not in self.obj: --> 244 raise KeyError(f"Column not found: {key}") 245 ndim = self.obj[key].ndim 246 return self._gotitem(key, ndim=ndim) KeyError: 'Column not found: 0'
In [47]:
for sex_group in grouped:
print(f'the type is: {type(sex_group)}\n')
print(f'the length is: {len(sex_group)}\n')
first_element = sex_group[0]
print(f'the first element is: {first_element}\n')
print(f'it has a type of: {type(sex_group[0])}\n')
second_element = sex_group[1]
print(f'the second element is: {second_element}\n')
print(f'it has a type of: {type(second_element)}\n')
print(f'what we hav: {sex_group}')
break
the type is: <class 'tuple'>
the length is: 2
the first element is: Male
it has a type of: <class 'str'>
the second element is: total_bill tip sex smoker day time size
24 19.82 3.18 Male No Sat Dinner 2
6 8.77 2.00 Male No Sun Dinner 2
153 24.55 2.00 Male No Sun Dinner 4
211 25.89 5.16 Male Yes Sat Dinner 4
176 17.89 2.00 Male Yes Sun Dinner 2
192 28.44 2.56 Male Yes Thur Lunch 2
9 14.78 3.23 Male No Sun Dinner 2
it has a type of: <class 'pandas.core.frame.DataFrame'>
what we hav: ('Male', total_bill tip sex smoker day time size
24 19.82 3.18 Male No Sat Dinner 2
6 8.77 2.00 Male No Sun Dinner 2
153 24.55 2.00 Male No Sun Dinner 4
211 25.89 5.16 Male Yes Sat Dinner 4
176 17.89 2.00 Male Yes Sun Dinner 2
192 28.44 2.56 Male Yes Thur Lunch 2
9 14.78 3.23 Male No Sun Dinner 2)
In [49]:
bill_sex_time = tips_10.groupby(['sex', 'time'])
group_avg = bill_sex_time.mean(numeric_only=True)
print(type(group_avg))
<class 'pandas.core.frame.DataFrame'>
In [50]:
print(group_avg)
total_bill tip size
sex time
Male Lunch 28.440000 2.560000 2.000000
Dinner 18.616667 2.928333 2.666667
Female Lunch 12.740000 2.260000 2.000000
Dinner 15.380000 3.000000 2.000000
In [51]:
print(group_avg.columns)
Index(['total_bill', 'tip', 'size'], dtype='object')
In [52]:
print(group_avg.index)
MultiIndex([( 'Male', 'Lunch'),
( 'Male', 'Dinner'),
('Female', 'Lunch'),
('Female', 'Dinner')],
names=['sex', 'time'])
In [53]:
group_method = tips_10.groupby(['sex', 'time']).mean(numeric_only=True).reset_index()
print(group_method)
sex time total_bill tip size 0 Male Lunch 28.440000 2.560000 2.000000 1 Male Dinner 18.616667 2.928333 2.666667 2 Female Lunch 12.740000 2.260000 2.000000 3 Female Dinner 15.380000 3.000000 2.000000
In [54]:
group_param = tips_10.groupby(['sex', 'time'], as_index=False).mean(numeric_only=True)
print(group_param)
sex time total_bill tip size 0 Male Lunch 28.440000 2.560000 2.000000 1 Male Dinner 18.616667 2.928333 2.666667 2 Female Lunch 12.740000 2.260000 2.000000 3 Female Dinner 15.380000 3.000000 2.000000
In [57]:
intv_df = pd.read_csv('../data/epi_sim.zip')
print(intv_df)
ig_type intervened pid rep sid tr 0 3 40 294524448 1 201 0.000135 1 3 40 294571037 1 201 0.000135 2 3 40 290699504 1 201 0.000135 3 3 40 288354895 1 201 0.000135 4 3 40 292271290 1 201 0.000135 ... ... ... ... ... ... ... 9434648 2 87 345636694 2 201 0.000166 9434649 3 87 295125214 2 201 0.000166 9434650 2 89 292571119 2 201 0.000166 9434651 3 89 292528142 2 201 0.000166 9434652 2 95 291956763 2 201 0.000166 [9434653 rows x 6 columns]
In [58]:
count_only = (
intv_df
.groupby(['rep', 'intervened', 'tr'])
['ig_type']
.count()
)
print(count_only)
rep intervened tr
0 8 0.000166 1
9 0.000152 3
0.000166 1
10 0.000152 1
0.000166 1
..
2 193 0.000135 1
0.000152 1
195 0.000135 1
198 0.000166 1
199 0.000135 1
Name: ig_type, Length: 1196, dtype: int64
In [59]:
print(type(count_only))
<class 'pandas.core.series.Series'>
In [60]:
print(count_only.index)
MultiIndex([(0, 8, 0.000166),
(0, 9, 0.000152),
(0, 9, 0.000166),
(0, 10, 0.000152),
(0, 10, 0.000166),
(0, 12, 0.000152),
(0, 12, 0.000166),
(0, 13, 0.000152),
(0, 13, 0.000166),
(0, 14, 0.000152),
...
(2, 187, 0.000152),
(2, 187, 0.000166),
(2, 189, 0.000135),
(2, 189, 0.000152),
(2, 190, 0.000166),
(2, 193, 0.000135),
(2, 193, 0.000152),
(2, 195, 0.000135),
(2, 198, 0.000166),
(2, 199, 0.000135)],
names=['rep', 'intervened', 'tr'], length=1196)
In [61]:
count_mean = count_only.groupby(level=[0, 1, 2]).mean()
print(count_mean.head())
rep intervened tr
0 8 0.000166 1.0
9 0.000152 3.0
0.000166 1.0
10 0.000152 1.0
0.000166 1.0
Name: ig_type, dtype: float64
In [62]:
count_mean = (
intv_df
.groupby(["rep", "intervened", "tr"])["ig_type"]
.count()
.groupby(level=[0, 1, 2])
.mean()
)
print(count_mean.head())
rep intervened tr
0 8 0.000166 1.0
9 0.000152 3.0
0.000166 1.0
10 0.000152 1.0
0.000166 1.0
Name: ig_type, dtype: float64
In [63]:
import matplotlib.pyplot as plt
fig = sns.lmplot(
data=count_mean.reset_index(),
x="intervened",
y="ig_type",
hue="rep",
col="tr",
fit_reg =False,
palette="viridis"
)
plt.show()
C:\Users\offse\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
In [64]:
cumulative_count = (
intv_df
.groupby(["rep", "intervened", "tr"])["ig_type"]
.count()
.groupby(level=["rep"])
.cumsum()
.reset_index()
)
fig = sns.lmplot(
data=cumulative_count,
x="intervened",
y="ig_type",
hue="rep",
col="tr",
fit_reg =False,
palette="viridis"
)
plt.show()
C:\Users\offse\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
'도서 > 프로그래밍' 카테고리의 다른 글
| [09][完] Do it! 데이터 분석을 위한 판다스 입문 (0) | 2024.01.09 |
|---|---|
| [08] Do it! 데이터 분석을 위한 판다스 입문 (0) | 2024.01.08 |
| [06] Do it! 데이터 분석을 위한 판다스 입문 (0) | 2024.01.07 |
| [05] Do it! 데이터 분석을 위한 판다스 입문 (0) | 2024.01.06 |
| [04] Do it! 데이터 분석을 위한 판다스 입문 (0) | 2024.01.05 |
