我想做兩件事：我想為每個(gè)日期/天創(chuàng)建一個(gè)箱線圖，其中包含該日期中 MeanTravelTimeSeconds 的所有值。MeanTravelTimeSeconds 元素的數(shù)量隨日期而變化（例如，某一天可能有 300 個(gè)值，而另一天可能有 400 個(gè)）。另外，我想將多索引系列中的行轉(zhuǎn)換為列，因?yàn)槲也幌Ｍ看味贾貜?fù)行。如果它保持這樣，我就會(huì)有數(shù)千萬不必要的行。這是在按日期索引的 df 上使用 df.stack() 后的結(jié)果系列（日期是日期時(shí)間對(duì)象索引）：Date 2016-01-02 NumericIndex 1611664 OriginMovementID 4744 DestinationMovementID 5084 MeanTravelTimeSeconds 1233 RangeLowerBoundTravelTimeSeconds 756 ... 2020-03-31 DestinationMovementID 3594 MeanTravelTimeSeconds 1778 RangeLowerBoundTravelTimeSeconds 1601 RangeUpperBoundTravelTimeSeconds 1973 DayOfWeek TuesdayLength: 11281655, dtype: object當(dāng)我使用seaborn繪制箱線圖時(shí)，在使用不同的選擇后，我遇到了很多錯(cuò)誤。如果我嘗試這樣做df.stack().unstack()或df.stack().T收到以下錯(cuò)誤：Index contains duplicate entries, cannot reshape如何繪制箱線圖以及如何將行轉(zhuǎn)換為列？
1 回答
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TA貢獻(xiàn)1934條經(jīng)驗(yàn) 獲得超2個(gè)贊
您確實(shí)需要使您的索引獨(dú)一無二，以使您想要的功能發(fā)揮作用。我建議在其他兩個(gè)關(guān)鍵列中每次更改時(shí)都會(huì)重置一個(gè)序列號(hào)。
import datetime as dt
import random
import numpy as np
cat = ["NumericIndex","OriginMovementID","DestinationMovementID","MeanTravelTimeSeconds",
"RangeLowerBoundTravelTimeSeconds"]
df = pd.DataFrame(
[{"Date":d, "Observation":cat[random.randint(0,len(cat)-1)],
"Value":random.randint(1000,10000)}
for i in range(random.randint(5,20))
for d in pd.date_range(dt.datetime(2016,1,2), dt.datetime(2016,3,31), freq="14D")])
# starting point....
df = df.sort_values(["Date","Observation"]).set_index(["Date","Observation"])
# generate an array that is sequential within change of key
seq = np.full(df.index.shape, 0)
s=0
p=""
for i, v in enumerate(df.index):
if i==0 or p!=v: s=0
else: s+=1
seq[i] = s
p=v
df["SeqNo"] = seq
# add to index - now unstack works as required
dfdd = df.set_index(["SeqNo"], append=True)
dfdd.unstack(0).loc["MeanTravelTimeSeconds"].boxplot()
print(dfdd.unstack(1).head().to_string())
輸出
Value
Observation DestinationMovementID MeanTravelTimeSeconds NumericIndex OriginMovementID RangeLowerBoundTravelTimeSeconds
Date SeqNo
2016-01-02 0 NaN NaN 2560.0 5324.0 5085.0
1 NaN NaN 1066.0 7372.0 NaN
2016-01-16 0 NaN 6226.0 NaN 7832.0 NaN
1 NaN 1384.0 NaN 8839.0 NaN
2 NaN 7892.0 NaN NaN NaN
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