Tasos - 1 year ago 160

Python Question

Let's say that I have a dataframe like this one

`import pandas as pd`

df = pd.DataFrame([[1, 2, 1], [1, 3, 2], [4, 6, 3], [4, 3, 4], [5, 4, 5]], columns=['A', 'B', 'C'])

>> df

A B C

0 1 2 1

1 1 3 2

2 4 6 3

3 4 3 4

4 5 4 5

The original table is more complicated with more columns and rows.

I want to get the first row that fulfil some criteria. Examples:

- Get first row where A > 3 (returns row 2)
- Get first row where A > 4 AND B > 3 (returns row 4)
- Get first row where A > 3 AND (B > 3 OR C > 2) (returns row 2)

But, if there isn't any row that fulfil the specific criteria, then I want to get the first one after I just sort it descending by A (or other cases by B, C etc)

- Get first row where A > 6 (returns row 4 by ordering it by A desc and get the first one)

I was able to do it by iterating on the dataframe (I know that craps :P). So, I prefer a more pythonic way to solve it.

Answer Source

This tutorial is a very good one for pandas slicing. Make sure you check it out. Onto some snippets... To slice a dataframe with a condition, you use this format:

```
>>> df[condition]
```

This will return a slice of your dataframe which you can index using `iloc`

. Here are your examples:

Get first row where A > 3 (returns row 2)

`>>> df[df.A > 3].iloc[0] A 4 B 6 C 3 Name: 2, dtype: int64`

If what you actually want is the row number, rather than using `iloc`

, it would be `df[df.A > 3].index[0]`

.

Get first row where A > 4 AND B > 3:

`>>> df[(df.A > 4) & (df.B > 3)].iloc[0] A 5 B 4 C 5 Name: 4, dtype: int64`

Get first row where A > 3 AND (B > 3 OR C > 2) (returns row 2)

`>>> df[(df.A > 3) & ((df.B > 3) | (df.C > 2))].iloc[0] A 4 B 6 C 3 Name: 2, dtype: int64`

Now, with your last case we can write a function that handles the default case of returning the descending-sorted frame:

```
>>> def series_or_default(X, condition, default_col, ascending=False):
... sliced = X[condition]
... if sliced.shape[0] == 0:
... return X.sort_values(default_col, ascending=ascending).iloc[0]
... return sliced.iloc[0]
>>>
>>> series_or_default(df, df.A > 6, 'A')
A 5
B 4
C 5
Name: 4, dtype: int64
```

As expected, it returns row 4.