I am trying to apply a function that returns an specific date in an specific format, however I am struggling to apply this function to a new pandas dataframe column. Here’s what I got so far: The next error arises: KeyError: datetime.datetime(2021, 2, 1, 0, 0) Expected output could be a pandas dataframe column where row-values are set_date output. How
Tag: pandas
Python Numpy: int arrays can be converted to a scalar index
Please help me to get out of this error, maybe, it’s duplicate but I could not set it for my code. ERROR dataset Answer I think you might want to select your X columns slightly differently, e.g.
Pandas dataframe (getting one value without the index)
From a pandas dataframe, I want just the value and not the index. Returns: Adding .to_string() Returns: I do not want to loop, how can I just get: Answer Always check the docs for the method you are using:
Pandas Column join list values
Using Python, Pandas, I have a couple columns that have lists in them, I want to convert the list to a string. Example:I have the following values in a record, in the field ev_connector_types [u’ACME’, u’QUICK_CONNECT’] I’d like the value for that record to show: ACME, QUICK_CONNECT, without the list brackets, the ‘u’, and the quotes. I’ve tried. But I’m
Pandas group by unique ID and Distinct date per unique ID
Title may be confusing: I have a dataframe that displays user_id sign in’s during the week. My goal is to display the de-duped ID along with the de-duped dates per employee, in order to get a count of # days the user uniquely signed in for the week. So I’ve been trying to enforce a rule to make sure I’m
Filter Pandas MultiIndex over all First Levels Columns
Trying to find a way of efficiently filtering all entries under both top level columns based on a filter defined for only one of the top level columns. Best explained with the example below and desired output. Example DataFrame Create filter for multiindex dataframe Desired output: Answer You can reshape for simplify solution by reshape for DataFrame by DataFrame.stack with
How to groupby 2 columns but order descending by count()
i have a dataframe and want to group 2 columns, which is working fine. Now the grouped dataframe is sorted by the CustomerID values. But i want to sort it by the count(). So that i have the Sektor then the CustomerIDs but the CustomerIds that occure the most should be at the top. So descending. Expected Output should be:
Uncommon rows based on a column in pandas
Suppose I have two dataframes: and I want to use the second df as reference and drop those rows that exist in df2 from df1, so the result would be I tried: but this gives me the following: Answer Use Series.isin with inverted mask by ~ in boolean indexing, working well if need test only one column: If need test
Optimizing a standard deviation function Pandas Numpy Python
The std pandas function below calculates the standard deviation of every nth value defined by number. So it would take the the values of PC_list with indexes [0,1,2,3,4,5] and calculate the standard deviation and then the indexes [1,2,3,4,5] and calculate the standard deviation until the end of PC_list. I am trying to optimize the code by trying to make it
return a list if the column contains a string
I would like to check if the Names column contains any of the strings in the kw. If yes, return the list. Here is the data: I’ve tried: But it returns: I am expecting an output like: Answer You were very close: