Pandas: clean up DataFrame filled with NaN's

Question

I have a DataFrame that has NaNs scattered throughout. I read here in the Pandas documentation (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html) that pd.dropna should remove all NaNs but it isn't working on my DataFrame.

Here is my data:

fish_frame:                         0       1       2         3  \
0                   735-8     NaN     NaN       NaN   
1                     NaN     NaN     NaN  LIVE WGT   
2                 GBE COD     NaN     NaN       600   
3                 GBW COD     NaN  11,189       NaN   
4                 GOM COD     NaN       0       NaN   
5                 POLLOCK     NaN     NaN     1,103   
6                   WHAKE     NaN     NaN        12   
7             GBE HADDOCK     NaN  10,730       NaN   
8             GBW HADDOCK     NaN  64,147       NaN   
9             GOM HADDOCK     NaN       0       NaN   
10                REDFISH     NaN     NaN         0   
11         WITCH FLOUNDER     NaN     370       NaN   
12                 PLAICE     NaN     NaN       622   
13     GB WINTER FLOUNDER  54,315     NaN       NaN   
14    GOM WINTER FLOUNDER     653     NaN       NaN   
15  SNEMA WINTER FLOUNDER  14,601     NaN       NaN   
16          GB YELLOWTAIL     NaN   1,663       NaN   
17       SNEMA YELLOWTAIL     NaN   1,370       NaN   
18       CCGOM YELLOWTAIL   1,812     NaN       NaN   

                            4   5      6   7  ASK           TRADE_DATE  \
0                         NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
1                         NaN NaN  TOTAL NaN    1  2013-05-15 10:09:00   
2                         NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
3                         NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
4   Package Deal - $40,753.69 NaN   None NaN    1  2013-05-15 10:09:00   
5                         NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
6                         NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
7                         NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
8                         NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
9                         NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
10                        NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
11                        NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
12                        NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
13                        NaN NaN   None NaN    1  2013-05-15 10:09:00   
14                        NaN NaN   None NaN    1  2013-05-15 10:09:00   
15                        NaN NaN   None NaN    1  2013-05-15 10:09:00   
16                        NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
17                        NaN NaN    NaN NaN    1  2013-05-15 10:09:00   
18                        NaN NaN   None NaN    1  2013-05-15 10:09:00

Ideally, I would like to see all the fish species line up in one column, as they are, and have their corresponding weights line up in one column alongside them. I THINK removing all the NaNs would accomplish that but I am failing to do so with the line fish_frame.dropna().

Any help would be appreciated, thanks.

An ideal printout would look something like this:

fish_frame2:                         0       1       2         3  \
0                   735-8        
1                       LIVE WGT   
2                 GBE COD     600   
3                 GBW COD     11,189       
4                 GOM COD     0       
5                 POLLOCK     1,103   
6                   WHAKE     12   
7             GBE HADDOCK     10,730       
8             GBW HADDOCK     64,147       
9             GOM HADDOCK     0       
10                REDFISH     0   
11         WITCH FLOUNDER     370       
12                 PLAICE     622   
13     GB WINTER FLOUNDER     54,315     
14    GOM WINTER FLOUNDER     653     
15  SNEMA WINTER FLOUNDER     14,601       
16          GB YELLOWTAIL     1,663       
17       SNEMA YELLOWTAIL     1,370       
18       CCGOM YELLOWTAIL     1,812     

Answer 1

Let's do a simple example.

import pandas as pd
import numpy as np
np.random.seed(4)
A=np.random.rand(6,4)
A=np.where(A<.7, np.nan,A)
df = pd.DataFrame(A)
print(df)
# result:
#           0         1         2         3
# 0  0.967030       NaN  0.972684  0.714816
# 1       NaN       NaN  0.976274       NaN
# 2       NaN       NaN  0.779383       NaN
# 3  0.862993  0.983401       NaN       NaN
# 4       NaN       NaN       NaN  0.956653
# 5       NaN  0.948977  0.786306  0.866289

Dropna will drop all the information, because all rows contain at least one NAN. dropna will drop all rows containing at least one NAN.

Depending on what you want to do with your data, you will have to subsample it. In your case with the columns 1 to 7. In my case I'll do it from 1 to 3.

sub = df[[i for i in range(1,4)]] # in your case 1 to 7
print(sub)
# result:
#           1         2         3
# 0       NaN  0.972684  0.714816
# 1       NaN  0.976274       NaN
# 2       NaN  0.779383       NaN
# 3  0.983401       NaN       NaN
# 4       NaN       NaN  0.956653
# 5  0.948977  0.786306  0.866289

Once your data is subsampled, you can select the operation you want to do with your data, let's say, use the maximum of every row you'll do the following:

print(sub.max(axis=1))
# result:
# 0    0.972684
# 1    0.976274
# 2    0.779383
# 3    0.983401
# 4    0.956653
# 5    0.948977
# dtype: float64

You can also use other methods such as min or if you want your custom and more sophisticated method you can use the function apply.

def first_element(x):
    if x.first_valid_index() is None:
        return None
    else:
        return x[x.first_valid_index()]

sub2=sub.apply(first_element,axis=1)
print(sub2)
# result
# 0    0.972684
# 1    0.976274
# 2    0.779383
# 3    0.983401
# 4    0.956653
# 5    0.948977

The important thing for you is what you want to do with the information of the relevant columns.