pandas 当在列中找到特定字符串时,将 Dataframe 切片到子 Dataframe 中

vlju58qv  于 2023-01-11  发布在  其他
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假设我有一个 Dataframe df,我想将它分割成多个 Dataframe ,并将每个 Dataframe 存储在一个列表(list_of_dfs)中。
每个子 Dataframe 应仅包含行“结果”。当列“点”中的值为“P1”且列“X_Y”中的值为“X”时,一个子 Dataframe 开始。
我试着先找到每个“P1”的索引,然后使用“P1”的索引在列表解析中分割整个 Dataframe 。但是我收到了一个有两个空 Dataframe 的列表。有人能给我建议吗?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)
3htmauhk

3htmauhk1#

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())]
  • 获取步骤等于“结果”的帧的子集
  • 检查哪些行有“P1”和“X”序列
  • 给出一个真/假序列
  • 它的累积和确定组作为“旋转”(转向)点将为True,因为在数值上下文中False == 0
  • 在GroupBy对象上迭代产生“group_label,sub_frame”对,我们从这些对中取出sub_frame

得到

>>> 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]

中间人是
一个一个二个一个一个一个三个一个一个一个一个一个四个一个

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