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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