Web2 days ago · For textual values, create a list of strings and iterate through the list, appending the desired string to each element. For numerical values, create a dataframe with specific ranges in each column, then use a for loop to add additional rows to the dataframe with calculated values based on the loop index.

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WebMay 31, 2024 · I rewrote the solution using DataFrame.apply instead of iterating, and as optimization used numpy arrays wherever possible. I used frozenset because they are immutable and hashable and thus Series.unique works properly.Series.unique fails on elements of type set.. Also, I found d.loc[list(x), 'STRIP'].nunique() to be slightly faster … WebIf True, return the index as the first element of the tuple. name str or None, default “Pandas” The name of the returned namedtuples or None to return regular tuples. Returns iterator. An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values. easter prayers written by children

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WebJul 16, 2024 · Example 1: Iterate Over All Columns in DataFrame The following code shows how to iterate over every column in a pandas DataFrame: for name, values in df. iteritems (): print (values) 0 25 1 12 2 15 3 14 4 19 Name: points, dtype: int64 0 5 1 7 2 7 3 9 4 12 Name: assists, dtype: int64 0 11 1 8 2 10 3 6 4 6 Name: rebounds, dtype: int64 Web2 days ago · For textual values, create a list of strings and iterate through the list, appending the desired string to each element. For numerical values, create a dataframe with … Web5 Answers. Sorted by: 147. One easy way would be to groupby the first level of the index - iterating over the groupby object will return the group keys and a subframe containing each group. In [136]: for date, new_df in df.groupby (level=0): ...: print (new_df) ...: observation1 observation2 date Time 2012-11-02 9:15:00 79.373668 224 9:16:00 ... culinary group