Reputation: 71
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?
Upvotes: 0
Views: 820
Reputation: 862661
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]
Upvotes: 1