M.Schreiber - 4 days ago 6

Python Question

Let's say I have a dataframe :

`d = pd.DataFrame({'Salary' : pd.Series([1, 20000, 5, 1000, 3000],`

index = ['Joe', 'Steph', 'Jared', 'Oliver', 'Gaby']),

'Sex' : pd.Series([0, 1, 0, 0, 1],

index=['Joe', 'Steph', 'Jared', 'Oliver', 'Gaby'])})

Salary Sex

Joe 1 0

Steph 20000 1

Jared 5 0

Oliver 7000 0

Gaby 3000 1

I write a function which takes in a column name as an argument, calculates the interquartile range of the values and returns the number of outliers based on that. If I would also like that function to return the number of females that have salaries that are outliers, how do I access the 'sex' column in order to check the corresponding 'sex' value of an outlier salary?

Here is my outlier function:

`def iqr_outliers(num_df, column):`

nan_count = 0

for value in column:

if value == 'NaN':

nan_count += 1

cleaned_column = [x for x in column if str(x) != "NaN"]

iqr = np.subtract(*np.percentile(cleaned_column, [75, 25]))

upper = np.percentile(cleaned_column, 75) + 1.5 * iqr

lower = np.percentile(cleaned_column, 25) - 1.5 * iqr

outliers = []

lows = 0

highs = 0

fem_outliers= 0

for value in cleaned_column:

if value < lower:

lows += 1

outliers.append(value)

elif value > upper:

highs += 1

outliers.append(value)

return ({"Number of low outliers": lows, "Number of high outliers": highs, "Number of NaNs": nan_count})

Somewhere in those if statements I want to see to check the value of 'sex' for the same row, but I really don't know how to access that.

Answer

Note, you can calculate inter-quartile range with `percentile`

:

```
In [21]: d
Out[21]:
Salary Sex
Joe 1 0
Steph 20000 1
Jared 5 0
Oliver 1000 0
Gaby 3000 1
In [22]: iqr = d.Salary.quantile([.25,.75]).values
In [23]: iqr
Out[23]: array([ 5., 3000.])
```

Then, you can use elementwise boolean operations:

```
In [24]: (d.Salary < iqr[0]) | (d.Salary > iqr[1])
Out[24]:
Joe True
Steph True
Jared False
Oliver False
Gaby False
Name: Salary, dtype: bool
```

And finally, you can use the result of that for selection on your entire dataframe:

```
In [26]: d[(d.Salary < iqr[0]) | (d.Salary > iqr[1])]
Out[26]:
Salary Sex
Joe 1 0
Steph 20000 1
```

Or something to that effect. I do not recall the specifics of Tukey outliers off hand. But it should be easily tractable with the approach illustrated above.

```
In [40]: IQR = iqr[1] - iqr[0]
In [41]: upper = 1.5*IQR+iqr[1]
In [42]: lower = iqr[0] - 1.5*IQR
In [43]: (d.Salary < lower) | (d.Salary > upper)
Out[43]:
Joe False
Steph True
Jared False
Oliver False
Gaby False
Name: Salary, dtype: bool
In [44]: d[(d.Salary < lower) | (d.Salary > upper)]
Out[44]:
Salary Sex
Steph 20000 1
```

To get the number of females, you can just use `sum`

```
In [46]: d[(d.Salary < lower) | (d.Salary > upper)]['Sex'].sum()
Out[46]: 1
```