import pandas as pd
# 讀取**資料集，檢視一月的資料
weight_loss = pd.read_csv('data/weight_loss.csv')
weight_loss.query('month == "jan"')
name
month
week
weight
0bob
janweek 1
2911
amyjan
week 1
1972
bobjan
week 2
2883
amyjan
week 2
1894
bobjan
week 3
2835
amyjan
week 3
1896
bobjan
week 4
2837
amyjan
week 4
190
# 定義乙個求**比例的函式
def find_perc_loss(s):
return (s - s.iloc[0]) / s.iloc[0]
# 檢視bob在一月的**成果
bob_jan = weight_loss.query('name=="bob" and month=="jan"')
find_perc_loss(bob_jan['weight'])
'''0 0.000000
2 -0.010309
4 -0.027491
6 -0.027491
name: weight, dtype: float64
'''
# 對name和month進行分組，然後使用transform方法，傳入函式，對數值進行轉換
pcnt_loss = weight_loss.groupby(['name', 'month'])['weight'].transform(find_perc_loss)
pcnt_loss.head(8)
0 0.000000
1 0.000000
2 -0.010309
3 -0.040609
4 -0.027491
5 -0.040609
6 -0.027491
7 -0.035533
name: weight, dtype: float64
# transform之後的結果，行數不變，可以賦值給原始dataframe作為乙個新列；
# 為了縮短輸出，只選擇bob的前兩個月資料
weight_loss['perc weight loss'] = pcnt_loss.round(3)
weight_loss.query('name=="bob" and month in ["jan", "feb"]')
name
month
week
weight
perc weight loss
0bob
janweek 1
2910.000
2bob
janweek 2
288-0.010
4bob
janweek 3
283-0.027
6bob
janweek 4
283-0.027
8bob
febweek 1
2830.000
10bob
febweek 2
275-0.028
12bob
febweek 3
268-0.053
14bob
febweek 4
268-0.053
# 因為最重要的是每個月的第4周，只選擇第4周的資料
week4 = weight_loss.query('week == "week 4"')
week4
name
month
week
weight
perc weight loss
6bob
janweek 4
283-0.027
7amy
janweek 4
190-0.036
14bob
febweek 4
268-0.053
15amy
febweek 4
173-0.089
22bob
marweek 4
261-0.026
23amy
marweek 4
170-0.017
30bob
aprweek 4
250-0.042
31amy
aprweek 4
161-0.053
# 用pivot重構dataframe，讓amy和bob的資料併排放置
winner = week4.pivot(index='month', columns='name', values='perc weight loss')
winner
amybob
month
apr-0.053
-0.042
feb-0.089
-0.053
jan-0.036
-0.027
mar-0.017
-0.026
# 用where方法選出每月的贏家
winner['winner'] = np.where(winner['amy'] < winner['bob'], 'amy', 'bob')
winner.style.highlight_min(axis=1)
amybob
winner
month
apr-0.053000
-0.042000
amyfeb
-0.089000
-0.053000
amyjan
-0.036000
-0.027000
amymar
-0.017000
-0.026000
bob
# 用value_counts()返回最後的比分
winner.winner.value_counts()
'''amy 3
bob 1
name: winner, dtype: int64
'''
# pandas預設是按字母排序的
week4a = week4.copy()
month_chron = week4a['month'].unique()
month_chron
#array(['jan', 'feb', 'mar', 'apr'], dtype=object)
# 轉換為categorical變數，可以做成按時間排序
week4a['month'] = pd.categorical(week4a['month'], categories=month_chron,ordered=true)
week4a.pivot(index='month', columns='name', values='perc weight loss')
amybob
month
jan-0.036
-0.027
feb-0.089
-0.053
mar-0.017
-0.026
apr-0.053
-0.042
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