How do I count the number of unique strings in a rolling window of a pandas dataframe?
a = pd.DataFrame(['a','b','a','a','b','c','d','e','e','e','e'])
a.rolling(3).apply(lambda x: len(np.unique(x)))
Output, same as original dataframe:
    0
0   a
1   b
2   a
3   a
4   b
5   c
6   d
7   e
8   e
9   e
10  e
Expected:
    0
0   1
1   2
2   2
3   2
4   2
5   3
6   3
7   3
8   2
9   1
10  1
                The unique function in pandas is used to find the unique values from a series. A series is a single column of a data frame. We can use the unique function on any possible set of elements in Python. It can be used on a series of strings, integers, tuples, or mixed elements.
You can use the nunique() function to count the number of unique values in a pandas DataFrame.
And you can use the following syntax to select unique rows across specific columns in a pandas DataFrame: df = df. drop_duplicates(subset=['col1', 'col2', ...])
How do you Count the Number of Occurrences in a data frame? To count the number of occurrences in e.g. a column in a dataframe you can use Pandas value_counts() method. For example, if you type df['condition']. value_counts() you will get the frequency of each unique value in the column “condition”.
I think you need first convert values to numeric - by factorize or by rank. Also min_periods parameter is necessary for avoid NaN in start of column:
a[0] = pd.factorize(a[0])[0]
print (a)
    0
0   0
1   1
2   0
3   0
4   1
5   2
6   3
7   4
8   4
9   4
10  4
b = a.rolling(3, min_periods=1).apply(lambda x: len(np.unique(x))).astype(int)
print (b)
    0
0   1
1   2
2   2
3   2
4   2
5   3
6   3
7   3
8   2
9   1
10  1
Or:
a[0] = a[0].rank(method='dense')
      0
0   1.0
1   2.0
2   1.0
3   1.0
4   2.0
5   3.0
6   4.0
7   5.0
8   5.0
9   5.0
10  5.0
b = a.rolling(3, min_periods=1).apply(lambda x: len(np.unique(x))).astype(int)
print (b)
    0
0   1
1   2
2   2
3   2
4   2
5   3
6   3
7   3
8   2
9   1
10  1
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