Pandas Dataframe - Shifting rows down and maintaining data

Question

My original Dataframe (df):

    column1  column2
0   1        a
1   2        b
2   3        c
3   4        d
4   5        e
5   6        f

I want to shift the values down by 6 like so:

    column1 column2
0       
1       
2       
3       
4       
5       
6   1        a
7   2        b
8   3        c
9   4        d
10  5        e
11  6        f

When I use df = df.shift(6), I end up loosing data.

I found this post (How to shift a column in Pandas DataFrame without losing value) but it only seems to work if the values are shifted down by 1.

How can I shift multiple spots down while retaining the data?

Vaishali · Accepted Answer · 2017-05-26 01:31:28Z

2

You can try

df.index = df.index+6
df = df.reindex(np.arange(12))

    column1 column2
0   NaN     NaN
1   NaN     NaN
2   NaN     NaN
3   NaN     NaN
4   NaN     NaN
5   NaN     NaN
6   1.0     a
7   2.0     b
8   3.0     c
9   4.0     d
10  5.0     e
11  6.0     f

answered May 26, 2017 at 1:31

Vaishali

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2 Comments

Allen Qin Over a year ago

Good thinking!!

andyb88 Over a year ago

Thanks, I ended up substituting 'df = df.reindex(np.arange(12))' with 'df = df.reindex(np.arange(max(df.index)+1))' to allow the dataset to be dynamic (which I think is working).

akuiper · Accepted Answer · 2017-05-26 01:44:08Z

0

shift doesn't change the shape of the data frame, what you are looking for is adding some extra rows in front of the data frame, for the simplest case you are showing (i.e. index is consisted of integers) you can manipulate the index as follows to achieve what you need:

df.index = df.index + 6
df.reindex(range(6) + df.index.tolist())    

#column1    column2
#0   NaN    NaN
#1   NaN    NaN
#2   NaN    NaN
#3   NaN    NaN
#4   NaN    NaN
#5   NaN    NaN
#6   1.0    a
#7   2.0    b
#8   3.0    c
#9   4.0    d
#10  5.0    e
#11  6.0    f

edited May 26, 2017 at 1:44

answered May 26, 2017 at 1:31

akuiper

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