Pandas/Python根据条件添加行 [英] Pandas/Python adding row based on condition
问题描述
我希望根据某些条件在两个现有行之间的数据框中插入一行.
I am looking to insert a row into a dataframe between two existing rows based on certain criteria.
例如，我的数据框:
import pandas as pd
df = pd.DataFrame({'Col1':['A','B','D','E'],'Col2':['B', 'C', 'E', 'F'], 'Col3':['1', '1', '1', '1']})
外观如下:
Col1 Col2 Col3
0 A B 1
1 B C 1
2 D E 1
3 E F 1
在满足以下条件的情况下，我希望能够在索引1和索引2之间插入新行:
I want to be able to insert a new row between Index 1 and Index 2 given the condition:
n = 0
while n < len(df):
(df.ix[n]['Col2'] == df.ix[n+1]['Col1']) == False
Something, Something, insert row
n+=1
我想要的输出表如下:
Col1 Col2 Col3
0 A B 1
1 B C 1
2 C D 1
3 D E 1
4 E F 1
我正在努力根据之前和后续记录中的值有条件地插入行.我最终希望在我的真实世界示例中进行上述练习，该示例包含多个条件，并保留一个以上列的值(在此示例中为Col3，但在我的真实世界中将是多个列)
I am struggling with conditional inserting of rows based on values in the previous and proceeding records. I ultimately want to preform the above exercise on my real world example which would include multiple conditions, and preserving the values of more than one column (in this example it was Col3, but in my real world it would be multiple columns)
推荐答案
更新:内存节省方法-首先为新行设置一个带间隙的新索引:
UPDATE: memory saving method - first set a new index with a gap for a new row:
In [30]: df
Out[30]:
Col1 Col2 Col3
0 A B 1
1 B C 1
2 D E 1
3 E F 1
如果要在索引1和2之间插入新行，我们将索引拆分到位置2:
if we want to insert a new row between indexes 1 and 2, we split the index at position 2:
In [31]: idxs = np.split(df.index, 2)
设置一个新索引(在位置2处有间隔):
set a new index (with gap at position 2):
In [32]: df.set_index(idxs[0].union(idxs[1] + 1), inplace=True)
In [33]: df
Out[33]:
Col1 Col2 Col3
0 A B 1
1 B C 1
3 D E 1
4 E F 1
插入索引为2的新行:
In [34]: df.loc[2] = ['X','X',2]
In [35]: df
Out[35]:
Col1 Col2 Col3
0 A B 1
1 B C 1
3 D E 1
4 E F 1
2 X X 2
排序索引:
In [38]: df.sort_index(inplace=True)
In [39]: df
Out[39]:
Col1 Col2 Col3
0 A B 1
1 B C 1
2 X X 2
3 D E 1
4 E F 1
PS，您还可以使用df.append(new_df)插入DataFrame而不是单行:
PS you also can insert DataFrame instead of single row using df.append(new_df):
In [42]: df
Out[42]:
Col1 Col2 Col3
0 A B 1
1 B C 1
2 D E 1
3 E F 1
In [43]: idxs = np.split(df.index, 2)
In [45]: new_df = pd.DataFrame([['X', 'X', 10], ['Y','Y',11]], columns=df.columns)
In [49]: new_df.index += idxs[1].min()
In [51]: new_df
Out[51]:
Col1 Col2 Col3
2 X X 10
3 Y Y 11
In [52]: df = df.set_index(idxs[0].union(idxs[1]+len(new_df)))
In [53]: df
Out[53]:
Col1 Col2 Col3
0 A B 1
1 B C 1
4 D E 1
5 E F 1
In [54]: df = df.append(new_df)
In [55]: df
Out[55]:
Col1 Col2 Col3
0 A B 1
1 B C 1
4 D E 1
5 E F 1
2 X X 10
3 Y Y 11
In [56]: df.sort_index(inplace=True)
In [57]: df
Out[57]:
Col1 Col2 Col3
0 A B 1
1 B C 1
2 X X 10
3 Y Y 11
4 D E 1
5 E F 1
老答案:
一种(多种)实现方法是将DF拆分并以所需顺序将其与所需DF连接起来:
One (among many) way to achieve that would be to split your DF and concatenate it together with needed DF in desired order:
原始DF:
In [12]: df
Out[12]:
Col1 Col2 Col3
0 A B 1
1 B C 1
2 D E 1
3 E F 1
将其分为两部分([0:1]，[2:end]):
let's split it into two parts ([0:1], [2:end]):
In [13]: dfs = np.split(df, [2])
In [14]: dfs
Out[14]:
[ Col1 Col2 Col3
0 A B 1
1 B C 1, Col1 Col2 Col3
2 D E 1
3 E F 1]
现在我们可以按所需顺序将其与新DF连接起来:
now we can concatenate it together with a new DF in desired order:
In [15]: pd.concat([dfs[0], pd.DataFrame([['C','D', 1]], columns=df.columns), dfs[1]], ignore_index=True)
Out[15]:
Col1 Col2 Col3
0 A B 1
1 B C 1
2 C D 1
3 D E 1
4 E F 1
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