Q:

Pandas Value counts showing same bin twice

I am new to Python and Pandas. 
I have a large DataFrame on which I am running value_counts on to check the output as follows:
df['Remaining'].value_counts().sort_index()

Below is the output, as you can see 50.0 appears twice, once 6817 times and once 4 times.  I tried exporting the out when remaining df['Remaining'] == 50 to see the difference but there no difference really.  Anything I am doing wrong? 
-225.0        2
-200.0        4
-150.0       14
-125.0        7
-100.0      346
-75.0       202
-50.0      1343
-25.0       527
 0.0      61167
 25.0     10467
 50.0      6817
 50.0         4
 75.0      5358
 100.0     3428
 125.0     1626
 150.0      598
 175.0      334
 200.0      223
 225.0      102
 250.0       69
 275.0       13
 350.0        1
 375.0        4
 400.0        3
 425.0        2
Name: Remaining steps, dtype: int64

A:

Without your data, I'll try to make a guess.  I think it is precision-point issue.  Your 2 different 50.0 bins may actually have different precision-point values, and the print-out doesn't show it.  Let's try this sample
df = pd. DataFrame({'num': [-12.5, 50.00000000004, 50.0, -25.10, 50.0]} , dtype=float)
print(df)

In [23]: 
    num
0 -12.5
1  50.0
2  50.0
3 -25.1
4  50.0

Print just show the round-up value.  Let's try value_counts.  It shows 2 identical 50.0 bins the same as your issue
df.num.value_counts().sort_index()

Out[27]:
-25.1    1
-12.5    1
 50.0    2
 50.0    1
Name: num, dtype: int64

Let's try round it up before value_counts
df.num.round(2).value_counts().sort_index()

Out[28]:
-25.1    1
-12.5    1
 50.0    3
Name: num, dtype: int64

So, I guess you may call round before value_counts on your series as above to see it is fixed.