在列中找到特定字符串时，对子数据帧中的数据帧进行切片

Slice Dataframe in sub-dataframes when specific string in column is found

提问人：Bakira 提问时间：1/8/2023 更新时间：1/8/2023 访问量：43

问：

假设我有数据帧 df，我想将其切成多个数据帧，并将每个数据帧存储在一个列表 （list_of_dfs） 中。

每个子数据帧应仅包含“Result”行。 当在“点”列中给出值“P1”时，在“X_Y”列中给出值“X”时，开始一个子数据帧。

我尝试了这个方法，首先找到每个“P1”的指示，然后使用“P1”的指示在列表推导中对整个数据帧进行切片。但是我收到一个包含两个空数据帧的列表。 有人可以建议吗？ 谢谢！

import pandas as pd

df = pd.DataFrame(
    {
        "Step": (
            "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "Result", "Result", "Result", "Result", "Result",
            "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "Result", "Result", "Result", "Result", "Result"
        ),
        "Point": (
            "P1", "P2", "P2", "P3", "P3", "P1", "P2", "P2", "P3", "P3", "P1", "P2", "P2", "P3", "P3",
            "P1", "P2", "P2", "P3", "P3", "P1", "P2", "P2", "P3", "P3", "P1", "P2", "P2", "P3", "P3",
        ),
        "X_Y": (
            "X", "X", "Y", "X", "Y",  "X", "X", "Y", "X", "Y", "X", "X", "Y", "X", "Y", 
            "X", "X", "Y", "X", "Y",  "X", "X", "Y", "X", "Y", "X", "X", "Y", "X", "Y",
        ),
        "Value A": (
            70, 68, 66.75, 68.08, 66.72, 70, 68, 66.75, 68.08, 66.72, 70, 68, 66.75, 68.08, 66.72,
            70, 68, 66.75, 68.08, 66.72, 70, 68, 66.75, 68.08, 66.72, 70, 68, 66.75, 68.08, 66.72, 
        ),
        "Value B": (
            70, 68, 66.75, 68.08, 66.72, 70, 68, 66.75, 68.08, 66.72, 70, 68, 66.75, 68.08, 66.72,
            70, 68, 66.75, 68.08, 66.72, 70, 68, 66.75, 68.08, 66.72, 70, 68, 66.75, 68.08, 66.72,
        ),
    }
)

dff = df.loc[df["Step"] == "Result"]

value = "P1"
tuple_of_positions = list()

result = dff.isin([value])

seriesObj = result.any()
columnNames = list(seriesObj[seriesObj == True].index)

for col in columnNames:
    rows = list(result[col][result[col] == True].index)
    for row in rows:
        tuple_of_positions.append((row, col))

length_of_one_df = (len(dff["Point"].unique().tolist()) * 2 ) - 1

list_of_dfs = [dff.iloc[x : x + length_of_one_df] for x in rows]

print(list_of_dfs)
Python Pandas 数据帧 拆分 切片

评论

答：

0赞 Mustafa Aydın 1/8/2023 #1 
sub    = df.query("Step == \"Result\"")
pivots = sub[["Point", "X_Y"]].eq(["P1", "X"]).all(axis=1)
out    = [fr for _, fr in sub.groupby(pivots.cumsum())]
  • 获取 Step 等于“Result”的帧的子集
  • 检查哪些行有“P1”和“X”序列
    • 给出一个 True/False 系列
    • 它的累积总和决定了该组，因为“枢轴”（转弯）点将为 True，因为在数字上下文中 False == 0
    • 遍历 GroupBy 对象会产生“group_label， sub_frame”对，我们从中提取sub_frames

获取

>>> out

[      Step Point X_Y  Value A  Value B
 10  Result    P1   X    70.00    70.00
 11  Result    P2   X    68.00    68.00
 12  Result    P2   Y    66.75    66.75
 13  Result    P3   X    68.08    68.08
 14  Result    P3   Y    66.72    66.72,
       Step Point X_Y  Value A  Value B
 25  Result    P1   X    70.00    70.00
 26  Result    P2   X    68.00    68.00
 27  Result    P2   Y    66.75    66.75
 28  Result    P3   X    68.08    68.08
 29  Result    P3   Y    66.72    66.72]

中间人在哪里

>>> sub

      Step Point X_Y  Value A  Value B
10  Result    P1   X    70.00    70.00
11  Result    P2   X    68.00    68.00
12  Result    P2   Y    66.75    66.75
13  Result    P3   X    68.08    68.08
14  Result    P3   Y    66.72    66.72
25  Result    P1   X    70.00    70.00
26  Result    P2   X    68.00    68.00
27  Result    P2   Y    66.75    66.75
28  Result    P3   X    68.08    68.08
29  Result    P3   Y    66.72    66.72

>>> pivots 

10     True
11    False
12    False
13    False
14    False
25     True
26    False
27    False
28    False
29    False
dtype: bool

# groups
>>> pivots.cumsum()

10    1
11    1
12    1
13    1
14    1
25    2
26    2
27    2
28    2
29    2
dtype: int32

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