I’m newbie to pyspark. I have pandas code like below. I have found ‘approxQuantile’ in pyspark 2.x but I didn’t find any such in pyspark 1.6.0 My sample input: df.show() df.collect() I have to loop the above logic for all input columns. Could anyone please suggest how to rewrite above …
Tag: pandas
pandas df – sort on index but exclude first column from sort
I want to sort this df on rows (‘bad job’) but I want to exclude the first column from the sort so it remains where it is: expected output: I don’t know to edit my code below to exclude the 1st column from the sort: Answer Use argsort with add 1 for possible add first value 0 by reindex for
Difference between transpose() and .T in Pandas
I have a sample of data: I want to display simple statistics of the dataset in pandas using describe() method. Output 1: Is there any difference between the two workflows when I am ending up with the same result? Output 2: References: Pandas | API documentation | pandas.DataFrame.transpose Answer There is no …
Check if all values in dataframe column are the same
I want to do a quick and easy check if all column values for counts are the same in a dataframe: In: Out: I want just a simple condition that if all counts = same value then print(‘True’). Is there a fast way to do this? Answer An efficient way to do this is by comparing the first value with
difference between “&” and “and” in pandas
I have some code that runs on a cron (via kubernetes) for several months now. Yesterday, part of my code didn’t work that normally does: This statement, all of a sudden, wasnt ‘True’ (both df_temp and df_temp4 have data in them: however, this worked: Was there some sort of code push that wou…
Transforming a pandas df to a parquet-file-bytes-object
I have a pandas dataframe and want to write it as a parquet file to the Azure file storage. So far I have not been able to transform the dataframe directly into a bytes which I then can upload to Azure. My current workaround is to save it as a parquet file to the local drive, then read it as
GroupBy columns on column header prefix
I have a dataframe with column names that start with a set list of prefixes. I want to get the sum of the values in the dataframe grouped by columns that start with the same prefix. The only way I could figure out how to do it was to loop through the prefix list, get the columns from the dataframe
lambda function to scale column in pandas dataframe returns: “‘float’ object has no attribute ‘min'”
I am just getting started in Python and Machine Learning and have encountered an issue which I haven’t been able to fix myself or with any other online resource. I am trying to scale a column in a pandas dataframe using a lambda function in the following way: and get the following error message: ‘…
How to create rank column in Python based on other columns
I have a python dataframe that looks like the following: This dataframe has been sorted in descending order by ‘transaction_count’. I want to create another column in that dataframe called ‘rank’ that contains the count of occurrences of cust_ID. My desired output would look something …
pandas merge columns to create new column with comma separated values
My dataframe has four columns with colors. I want to combine them into one column called “Colors” and use commas to separate the values. For example, I’m trying to combine into a Colors column like this : My code is: But the output for ID 120 is: And the output for ID 121 is: FOUND MY PROBLE…