根据值将一个数据帧拆分为多个具有相同列标题的数据帧

Question

I have a dataframe that looks like below

+------+------+---+---+---+
| S.No | A    | B | C | D |
+------+------+---+---+---+
| 1    | 0.25 | 2 | 1 | 5 |
+------+------+---+---+---+
| 2    | 1.1  | 4 | 2 | 5 |
+------+------+---+---+---+
| 3    | 1.5  | 6 | 3 | 5 |
+------+------+---+---+---+
| 4    | 0.32 | 3 | 4 | 5 |
+------+------+---+---+---+
| 5    | 1.45 | 5 | 5 | 5 |
+------+------+---+---+---+
| 6    | 1.9  | 7 | 6 | 5 |
+------+------+---+---+---+
| 7    | 0.5  | 3 | 4 | 5 |
+------+------+---+---+---+
| 8    | 1.49 | 5 | 5 | 5 |
+------+------+---+---+---+

I want to split them into 3 dataframes with same column header value name, the split is based on Column A value ie 1st dataframe should start from 0.25 and end in 1.5 , the second dataframe should start from 0.32 and end in 1.9 and 3rd dataframe should start from 0.5 and end in 1.49 . ie when the value in column A is between 0-1 , the split should start, They all should retain the same column header value. Expected Output is as follows, Since i am new to this, i dont know how to get this done properly, any help in this would be appreciated.

Dataframe 1:

+------+------+---+---+---+
| S.No | A    | B | C | D |
+------+------+---+---+---+
| 1    | 0.25 | 2 | 1 | 5 |
+------+------+---+---+---+
| 2    | 1.1  | 4 | 2 | 5 |
+------+------+---+---+---+
| 3    | 1.5  | 6 | 3 | 5 |
+------+------+---+---+---+

Dataframe 2:

+------+------+---+---+---+
| S.No | A    | B | C | D |
+------+------+---+---+---+
| 4    | 0.32 | 3 | 4 | 5 |
+------+------+---+---+---+
| 5    | 1.45 | 5 | 5 | 5 |
+------+------+---+---+---+
| 6    | 1.9  | 7 | 6 | 5 |
+------+------+---+---+---+

Dataframe 3:

+------+------+---+---+---+
| S.No | A    | B | C | D |
+------+------+---+---+---+
| 7    | 0.5  | 3 | 4 | 5 |
+------+------+---+---+---+
| 8    | 1.49 | 5 | 5 | 5 |
+------+------+---+---+---+

Answer 1

让我们做cumsum

d={x: y for x , y in df.groupby(df.A.between(0,1).cumsum())}

Answer 2

You start off by identifying the indices where the values are between 0 and 1. This is done with a combination of between and index . Once you have the indices, you can start splitting the dataframe using the iloc method

#Identifies indices based on variable A
splitIndices = df.index[df.A.between(0,1)].tolist()


dfList = []

for i in range(len(splitIndices)-1):
    startIndex = splitIndices[i]
    endIndex = splitIndices[i+1]

    tempDf = df.iloc[startIndex : endIndex]

    #Appends the dataframe subset to the output list
    dfList.append(tempDf.copy())

Answer 3

According to the explanation you have provided, you include a between condition, eg:

1st dataframe should start from 0.25 and end in 1.5

this means values like 0.32 should be included in the dataframe

With that logic you can do the below:

l=[.25,1.5,.32,1.9,.5,1.49]
r=[(a,b) for a,b in zip(l[::2],l[1::2])]
for i in r:
    r i in r:
    print(df[df['A'].between(*i,inclusive=True)].sort_values('A'))
    print("----------------------------------")

---

   S.No     A    B    C    D
0   1.0  0.25  2.0  1.0  5.0
3   4.0  0.32  3.0  4.0  5.0
6   7.0  0.50  3.0  4.0  5.0
1   2.0  1.10  4.0  2.0  5.0
4   5.0  1.45  5.0  5.0  5.0
7   8.0  1.49  5.0  5.0  5.0
2   3.0  1.50  6.0  3.0  5.0
----------------------------------
   S.No     A    B    C    D
3   4.0  0.32  3.0  4.0  5.0
6   7.0  0.50  3.0  4.0  5.0
1   2.0  1.10  4.0  2.0  5.0
4   5.0  1.45  5.0  5.0  5.0
7   8.0  1.49  5.0  5.0  5.0
2   3.0  1.50  6.0  3.0  5.0
5   6.0  1.90  7.0  6.0  5.0
----------------------------------
   S.No     A    B    C    D
6   7.0  0.50  3.0  4.0  5.0
1   2.0  1.10  4.0  2.0  5.0
4   5.0  1.45  5.0  5.0  5.0
7   8.0  1.49  5.0  5.0  5.0