合并熊猫数据框：在左侧创建空列

Question

I have several datasets, which I am trying to merge into one. Below, I created fictive simpler smaller datasets to test the method and it worked perfectly fine.

examplelog = pd.DataFrame({'Depth':[10,20,30,40,50,60,70,80], 
                       'TVD':[10,19.9,28.8,37.7,46.6,55.5,64.4,73.3],
                       'T1':[11,11.3,11.5,12.,12.3,12.6,13.,13.8],
                       'T2':[11.3,11.5,11.8,12.2,12.4,12.7,13.1,14.1]})

log1 = pd.DataFrame({'Depth':[30,40,50,60],'T3':[12.1,12.6,13.7,14.]})


log2 = pd.DataFrame({'Depth':[20,30,40,50,60],'T4':[12.0,12.2,12.4,13.2,14.1]})

logs=[log1,log2]

result=examplelog.copy()

for i in logs:
    result=result.merge(i,how='left', on='Depth')
print result

The result is, as expected:

Depth    T1    T2   TVD    T3    T4
0     10  11.0  11.3  10.0   NaN   NaN
1     20  11.3  11.5  19.9   NaN  12.0
2     30  11.5  11.8  28.8  12.1  12.2
3     40  12.0  12.2  37.7  12.3  12.4
4     50  12.3  12.4  46.6  13.5  13.2
5     60  12.6  12.7  55.5  14.2  14.1
6     70  13.0  13.1  64.4   NaN   NaN
7     80  13.8  14.1  73.3   NaN   NaN

Happy with the result, I applied this method to my actual data, but for T3 and T4 in the resulting dataframes, I received just empty columns (all values were NaN). I suspect that the problem is with floating numbers, because my datasets were created on different machines by different software and although the "Depth" has the precision of two decimal numbers in all of the files, I am afraid that it may not be 20.05 in both of them, but one might be 20.049999999999999 while in the other it might be 20.05000000000001. Then, the merge function will not work, as shown in the following example:

examplelog = pd.DataFrame({'Depth':[10,20,30,40,50,60,70,80], 
                           'TVD':[10,19.9,28.8,37.7,46.6,55.5,64.4,73.3],
                           'T1':[11,11.3,11.5,12.,12.3,12.6,13.,13.8],
                           'T2':[11.3,11.5,11.8,12.2,12.4,12.7,13.1,14.1]})

log1 = pd.DataFrame({'Depth':[30.05,40.05,50.05,60.05],'T3':[12.1,12.6,13.7,14.]})


log2 = pd.DataFrame({'Depth':[20.01,30.01,40.01,50.01,60.01],'T4':[12.0,12.2,12.4,13.2,14.1]})

logs=[log1,log2]

result=examplelog.copy()

for i in logs:
    result=result.merge(i,how='left', on='Depth')
print result

   Depth    T1    T2   TVD  T3  T4
0     10  11.0  11.3  10.0 NaN NaN
1     20  11.3  11.5  19.9 NaN NaN
2     30  11.5  11.8  28.8 NaN NaN
3     40  12.0  12.2  37.7 NaN NaN
4     50  12.3  12.4  46.6 NaN NaN
5     60  12.6  12.7  55.5 NaN NaN
6     70  13.0  13.1  64.4 NaN NaN
7     80  13.8  14.1  73.3 NaN NaN

Do you know how to fix this? Thanks!

Answer 1

Round the Depth values to the appropriate precision:

for df in [examplelog, log1, log2]:
    df['Depth'] = df['Depth'].round(1)

---

import numpy as np
import pandas as pd

examplelog = pd.DataFrame({'Depth':[10,20,30,40,50,60,70,80], 
                           'TVD':[10,19.9,28.8,37.7,46.6,55.5,64.4,73.3],
                           'T1':[11,11.3,11.5,12.,12.3,12.6,13.,13.8],
                           'T2':[11.3,11.5,11.8,12.2,12.4,12.7,13.1,14.1]})

log1 = pd.DataFrame({'Depth':[30.05,40.05,50.05,60.05],'T3':[12.1,12.6,13.7,14.]})
log2 = pd.DataFrame({'Depth':[20.01,30.01,40.01,50.01,60.01],
                     'T4':[12.0,12.2,12.4,13.2,14.1]})

for df in [examplelog, log1, log2]:
    df['Depth'] = df['Depth'].round(1)

logs=[log1,log2]
result=examplelog.copy()
for i in logs:
    result=result.merge(i,how='left', on='Depth')
print(result)

yields

   Depth    T1    T2   TVD    T3    T4
0     10  11.0  11.3  10.0   NaN   NaN
1     20  11.3  11.5  19.9   NaN  12.0
2     30  11.5  11.8  28.8  12.1  12.2
3     40  12.0  12.2  37.7  12.6  12.4
4     50  12.3  12.4  46.6  13.7  13.2
5     60  12.6  12.7  55.5  14.0  14.1
6     70  13.0  13.1  64.4   NaN   NaN
7     80  13.8  14.1  73.3   NaN   NaN

---

Per the comments, rounding does not appear to work for the OP on the actual data. To debug the problem, find some rows which should merge:

subframes = []
for frame in [examplelog, log2]:
    mask = (frame['Depth'] < 20.051) & (frame['Depth'] >= 20.0)
    subframes.append(frame.loc[mask])

Then post

for frame in subframes:
    print(frame.to_dict('list')) 
    print(frame.info())          # shows the dtypes of the columns

This might give us the info we need to reproduce the problem.