CODEWITHSUNDEEP
pythonpandas
PEBKAC
PEBKAC

Reputation: 788

Add n empty rows in a dataframe

I would like to expand this dataframe on a depth range which has such a depth column:

import numpy as np
import pandas as pd

depth = np.array([0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5])    

df1 = pd.DataFrame({'depth': [0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2],
           '400.0': [13.909261, 7.758734, 3.513627, 2.095409, 1.628918, 0.782643, 0.278548, 0.160153, -0.155895, -0.152373, -0.147820, -0.023997, 0.010729, 0.006050, 0.002356],
           '401.0': [14.581624, 8.173803, 3.757856, 2.223524, 1.695623, 0.818065, 0.300235, 0.173674, -0.145402, -0.144456, -0.142969, -0.022471, 0.010802, 0.006181, 0.002641],
           '402.0': [15.253988, 8.588872, 4.002085, 2.351638, 1.762327, 0.853486, 0.321922, 0.187195, -0.134910, -0.136539, -0.138118, -0.020945, 0.010875, 0.006313, 0.002927],
           '403.0': [15.633908, 8.833914, 4.146499, 2.431543, 1.798185, 0.874350, 0.333470, 0.192128, -0.130119, -0.134795, -0.136049, -0.019307, 0.012037, 0.006674, 0.003002],
           '404.0': [15.991816, 9.066159, 4.283401, 2.507818, 1.831721, 0.894119, 0.344256, 0.196415, -0.125758, -0.133516  , -0.134189, -0.017659, -0.013281,0.007053, 0.003061],
           '405.0': [16.349725, 9.298403, 4.420303, 2.584094, 1.865257, 0.913887, 0.355041, 0.200702, -0.121396, -0.132237, -0.132330, -0.016012, 0.014525, 0.007433, 0.003120]
           })

So what I need in this case is three additional rows at the bottom with NaN values.

Similarly I have a df2 with depth range from 1.1 to 2.5 and need to fill the upper 3 rows based on the extended depth range.

How do I do it?

Upvotes: 2

Views: 558

Answers (3)

rafaelc
rafaelc

Reputation: 59284

Using combine_first

>>> pd.DataFrame({'depth':depth}).combine_first(df1)

Using pd.concat

>>> pd.concat([pd.DataFrame({'depth':depth}), df1.iloc[:,1:]], 1)

Upvotes: 2

BENY
BENY

Reputation: 323376

You can using merge 

pd.DataFrame({'depth':depth}).merge(df1,how='left')

Upvotes: 3

sacuL
sacuL

Reputation: 51425

One easy way to do it is to set the index to depth then reindex using your depth array:

df1.set_index('depth').reindex(depth).reset_index()


    depth      400.0      401.0      402.0      403.0      404.0      405.0
0     0.8  13.909261  14.581624  15.253988  15.633908  15.991816  16.349725
1     0.9   7.758734   8.173803   8.588872   8.833914   9.066159   9.298403
2     1.0   3.513627   3.757856   4.002085   4.146499   4.283401   4.420303
3     1.1   2.095409   2.223524   2.351638   2.431543   2.507818   2.584094
4     1.2   1.628918   1.695623   1.762327   1.798185   1.831721   1.865257
5     1.3   0.782643   0.818065   0.853486   0.874350   0.894119   0.913887
6     1.4   0.278548   0.300235   0.321922   0.333470   0.344256   0.355041
7     1.5   0.160153   0.173674   0.187195   0.192128   0.196415   0.200702
8     1.6  -0.155895  -0.145402  -0.134910  -0.130119  -0.125758  -0.121396
9     1.7  -0.152373  -0.144456  -0.136539  -0.134795  -0.133516  -0.132237
10    1.8  -0.147820  -0.142969  -0.138118  -0.136049  -0.134189  -0.132330
11    1.9  -0.023997  -0.022471  -0.020945  -0.019307  -0.017659  -0.016012
12    2.0   0.010729   0.010802   0.010875   0.012037  -0.013281   0.014525
13    2.1   0.006050   0.006181   0.006313   0.006674   0.007053   0.007433
14    2.2   0.002356   0.002641   0.002927   0.003002   0.003061   0.003120
15    2.3        NaN        NaN        NaN        NaN        NaN        NaN
16    2.4        NaN        NaN        NaN        NaN        NaN        NaN
17    2.5        NaN        NaN        NaN        NaN        NaN        NaN

Upvotes: 2

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