通過這篇文章，我正在尋找輸入來矢量化我的 python 代碼，該代碼目前使用兩個 for 循環。出于性能原因，我想避免使用 for 循環。我當前的工作 python 代碼如下所示。代碼有什么作用？我有一個帶有 c1 列的輸入數據框，它有 4 行 10 行和三行 20 行。c2 列是另一列帶有一些隨機數的列。預期輸出：我的窗口大小為 2。因此，對于 c1 = 10 或 c1=20 的每 2 行，我必須計算相應列 c2 的平均值。我附上了輸入和預期輸出的屏幕截圖。目前，我正在使用兩個 for 循環來實現這一點。輸入數據框截圖：輸入數據框 預期輸出截圖：預期輸出我當前的 Python 代碼：import pandas as pddata = [{'c1':10, 'c2':10},{'c1':10,'c2':20},{'c1':10,'c2':30},{'c1':10,'c2':40}, {'c1':20,'c2':50},{'c1':20,'c2':60},{'c1':20,'c2':70}]df = pd.DataFrame(data) # df = Inputdf.head() window = 2allDF = pd.DataFrame()records = df['c1'].unique()for x in records: intervalsDF = pd.DataFrame(columns=['c1','meanc2']) df2 = df.loc[df['c1'] == x] for i in range(0, len(df2), window): intervalIndex = len(intervalsDF) interval = df2[i:i+window] c1 = list(interval['c1'])[0] meanc2 = interval['c2'].mean() intervalSummary = [c1,meanc2] intervalsDF.loc[intervalIndex] = intervalSummary allDF = allDF.append(intervalsDF) # allDF is the expected outputallDF.head()
1 回答
aluckdog
TA貢獻1847條經驗 獲得超7個贊
可能有一種更短、更簡單的方法來執行轉換。但這里有一種避免循環的方法。
# create the data frame, as per the original post
data = [{'c1':10, 'c2':10},
{'c1':10,'c2':20},
{'c1':10,'c2':30},
{'c1':10,'c2':40},
{'c1':20,'c2':50},
{'c1':20,'c2':60},
{'c1':20,'c2':70}
]
df = pd.DataFrame(data) # df = Input
# 1. convert the index to an ordinary column
df = df.reset_index()
# 2. 'helper' is a column that counts 0, 1, 2, 3, ...
# and re-starts for each c1
df['helper'] = df['index'] - df.groupby('c1')['index'].transform(min)
# 3. integer division on 'helper', to get 0, 0, 1, 1, 2, 2, ...
# (identify non-overlapping pairs)
df['helper'] //= 2
# 4. now convert 'index' from ordinary column back to an Index
df = df.set_index('index')
# 5. compute the mean of c2 for value of 'c1' and each pair of observations
df = df.groupby(['c1', 'helper'])['c2'].mean()
# 6. re-order 'helper' and 'c1' to match order in output
df.index = df.index.swaplevel()
print(df)
helper c1
0 10 15
1 10 35
0 20 55
1 20 70
Name: c2, dtype: int64
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