从python中的特定索引下移特定列的行

Question

I am scraping multiple tables from multiple pages of a website. The issue is there is a row missing from the initial table. Basically, this is how the dataframe looks.

                               mar2018 feb2018 jan2018 dec2017 nov2017                              
                                                                                      oct2017 sep2017 aug2017  

                balls faced      345     561    295       0      645     balls faced    200    58      0
                runs scored      156     281    183       0      389     runs scored    50     20      0
                strike rate      52.3    42.6   61.1      0      52.2    strike rate    25     34      0
                dot balls        223     387    173       0      476     dot balls      125    34      0
                fours            8       12     19        0      22      sixes          2      0       0   
                doubles          20      38     16        0      36      fours          4      2       0
                notout           2       0      0         0      4       doubles        2      0       0
                                                                         notout         4      2       0

the column 'sixes' is missing in the first page and present in the subsequent pages. So, I am trying to move the rows starting from 'fours' to 'not out' to a position down and leave nan's in row 4 for first 5 columns starting from mar2018 to nov2017.

I tried the following code but it isn't working. This is moving the values horizontally but not vertically downward.

df.iloc[4][0:6] = df.iloc[4][0:6].shift(1)

and also

df2 =  pd.DataFrame(index = 4)
df = pd.concat([df.iloc[:], df2, df.iloc[4:]]).reset_index(drop=True)

did not work.

df['mar2018'] = df['mar2018'].shift(1)

But this moves all the values of that column down by 1 row.

So, I was wondering if it is possible to shift down rows of specific columns from a specific index?

Answer 1

I think need reindex by union by numpy.union1d of all index values:

idx = np.union1d(df1.index, df2.index)

df1 = df1.reindex(idx)
df2 = df2.reindex(idx)

---

print (df1)
             mar2018  feb2018  jan2018  dec2017  nov2017
balls faced    345.0    561.0    295.0      0.0    645.0
dot balls      223.0    387.0    173.0      0.0    476.0
doubles         20.0     38.0     16.0      0.0     36.0
fours            8.0     12.0     19.0      0.0     22.0
notout           2.0      0.0      0.0      0.0      4.0
runs scored    156.0    281.0    183.0      0.0    389.0
sixes            NaN      NaN      NaN      NaN      NaN
strike rate     52.3     42.6     61.1      0.0     52.2

print (df2)
             oct2017  sep2017  aug2017
balls faced      200       58        0
dot balls        125       34        0
doubles            2        0        0
fours              4        2        0
notout             4        2        0
runs scored       50       20        0
sixes              2        0        0
strike rate       25       34        0

If multiple DataFrame s in list is possible use list comprehension :

from functools import reduce

dfs = [df1, df2]
idx = reduce(np.union1d, [x.index for x in dfs])

dfs1 = [df.reindex(idx) for df in dfs]

---

print (dfs1)
[             mar2018  feb2018  jan2018  dec2017  nov2017
balls faced    345.0    561.0    295.0      0.0    645.0
dot balls      223.0    387.0    173.0      0.0    476.0
doubles         20.0     38.0     16.0      0.0     36.0
fours            8.0     12.0     19.0      0.0     22.0
notout           2.0      0.0      0.0      0.0      4.0
runs scored    156.0    281.0    183.0      0.0    389.0
sixes            NaN      NaN      NaN      NaN      NaN
strike rate     52.3     42.6     61.1      0.0     52.2,      oct2017  sep2017  aug2017
balls faced      200       58        0
dot balls        125       34        0
doubles            2        0        0
fours              4        2        0
notout             4        2        0
runs scored       50       20        0
sixes              2        0        0
strike rate       25       34        0]