Detecting Value Crossing Between Columns In Pandas

Let's say I have the following dataframe: df = pd.DataFrame({'a': [10, 20, 30, 40, 50], 'b': [0, 10, 40, 45, 50]}, columns = ['a', 'b']) I would like to make a list of indices whe

Solution 1:

You can use the Series.shift with Series.lt which is "less than", same as < and Series.ge which is "greater than or equal" and is same as >=:

mask = df['a'].shift().lt(df['b']) & df['a'].ge(df['b'])
# same as (df['A'].shift() < df['b']) & (df['a'] >= df['b'])

0False1False2False3False4True
dtype: bool

Notice, we don't have to specify astype(bool), pandas works with boolean indexing and returns booleans when defining conditions.

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To get the indices of the rows with True, use:

idx = df[mask].index.tolist()

print(idx)
[4]