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I have the following dataframe: 

df=pd.DataFrame({'seq':[0,1,2,3,4,5], 'location':['cal','cal','cal','il','il','il'],'lat':[29,29.1,28.2,15.2,15.6,14], 'lon':[-95,-98,-95.6,-88, -87.5,-88.9], 'name': ['mike', 'john', 'tyler', 'rob', 'ashley', 'john']})

I am wondering if there is a way to insert a new row at the beginning of the dataframe even though some fields may be missing in the new row.

I searched SO and found related links. add a row at top in pandas dataframe

However, my situation is different in that I don't have values for all the fields in my new row that I am inserting. Following link solves the same issue but in R: Inserting rows into data frame when values missing in category

How may I insert the following row in the above df? {'location' : 'warehouse', 'lat': 22, 'lon': -50}

My desired output is the following: 

   seq   location   lat   lon    name
0       warehouse  25.0 -50.0        
1  0.0        cal  29.0 -95.0    mike
2  1.0        cal  29.1 -98.0    john
3  2.0        cal  28.2 -95.6   tyler
4  3.0         il  15.2 -88.0     rob
5  4.0         il  15.6 -87.5  ashley
6  5.0         il  14.0 -88.9    john

The number of columns of my actual dataframe is quite large. Hence not feasible to insert a np.nan for each column. Looking for a way to just specify the fields and associated values and the remaining fields get populated with nans.

ds_student
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  • Insert `None` or `np.nan` for the missing values – G. Anderson Jun 27 '19 at 19:59
  • Hello @G.Anderson, I am showing only a representative dataframe. My actual dataframe has over 300 columns. Hence wanted to see if there is a easier way to add the row instead of np.nan for each missing field. – ds_student Jun 27 '19 at 20:00

2 Answers2

4

Try this:

import pandas as pd
import numpy as np
df=pd.DataFrame({'seq':[0,1,2,3,4,5], 'location':['cal','cal','cal','il','il','il'],'lat':[29,29.1,28.2,15.2,15.6,14], 'lon':[-95,-98,-95.6,-88, -87.5,-88.9], 'name': ['mike', 'john', 'tyler', 'rob', 'ashley', 'john']})

df_new1 = pd.DataFrame({'location' : ['warehouse'], 'lat': [22], 'lon': [-50]}) # sample data row1
df = pd.concat([df_new1, df], sort=False).reset_index(drop = True)
print(df) 

df_new2 = pd.DataFrame({'location' : ['abc'], 'lat': [28], 'name': ['abcd']}) # sample data row2
df = pd.concat([df_new2, df], sort=False).reset_index(drop = True) 
print(df)

output:

    lat   location   lon    name  seq
0  22.0  warehouse -50.0     NaN  NaN
0  29.0        cal -95.0    mike  0.0
1  29.1        cal -98.0    john  1.0
2  28.2        cal -95.6   tyler  2.0
3  15.2         il -88.0     rob  3.0
4  15.6         il -87.5  ashley  4.0
5  14.0         il -88.9    john  5.0

    lat   location    name   lon  seq
0  28.0        abc    abcd   NaN  NaN
1  22.0  warehouse     NaN -50.0  NaN
2  29.0        cal    mike -95.0  0.0
3  29.1        cal    john -98.0  1.0
4  28.2        cal   tyler -95.6  2.0
5  15.2         il     rob -88.0  3.0
6  15.6         il  ashley -87.5  4.0
7  14.0         il    john -88.9  5.0
Anubhav Singh
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0

You can first transform your dict to a dict of lists:

dic = {k, [v] for k,v in dic.items()}

And then

pandas.concat([pandas.DataFrame(dic), df])
Lucca Portes
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