I have the below dataframe which comes from a JSON trying to format ready for db insertion, i am splitting using .tolist() but getting error for None entries. tried fillna and replace to insert a dummy list i.e. [0,0,0] but will only let me replace with a string. Any suggestions welcome. this works #df_split_batl = df_split_batl.fillna(‘xx’) #df_split_batl = df_split_batl.replace(‘xx’,’yy’) but
Tag: dataframe
compare 2 string columns in data frame and add 1 to key if different
I’m having a hard time trying to figure this out, I have a data frame with multiple columns after merging 2. I have 2 list: I need to compare each group of variables and if they are different add 1 to the key column I was trying to use something like this: Answer I would avoid loops. Is there any
Python: How to filter a Pandas DataFrame using Values from a Series?
Context I am currently processing some data and encountered a problem. I would like to filter a Pandas DataFrame using Values from a Series. However, this always throws the following Error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). Code Question Does anyone have an idea what’s this error means and how
Pandas split list upon DataFrame creation
I have a JSON file coming in, which I am doing some operations/trimming on. The result looks like this: When applying df = pd.DataFrame(user, index=[0]) I get the following Dataframe: When applying df = pd.DataFrame(user) I get: I am aware, as to why that happens, however none is what I want. I’d like the following: However I am not sure
What is pandas equivalent of the following SQL?
OK, I have a dataframe that looks like the following: In SQL, to filter unique segments (segment_id) by travelmode I will do: What is the pandas equivalent of this expression? Answer Maybe: as suggested in this post.
Pandas get difference from first row at a set dtime with groupby
If I have a dataframe with [Group], [DTime] and [Value] columns For each [Group] I’m trying to find the difference between the first [Value] and every subsequent value from a set [DTime], for this example say it’s the start of the df at 2015-01-01. Ultimately I would like to plot a timeseries of [Difference] with a trace for each [Group]
Add averages to existing plot with pandas.DataFrame
I have a pandas data-frame of the form and I want to plot the last 7 days together with the average over the weekdays. I can create / plot the average by using and I can create / plot the last 7 days by using but I fail to combine them to a single plot since the average uses weekday
Pandas Dataframe: Retrieve the Maximum Value in a Pandas Dataframe using .groupby and .idxmax()
I have a Pandas Dataframe that contains a series of Airbnb Prices grouped by neighbourhood group neighbourhood and room_type. My objective is to return the Maximum Average Price for each room_type per Neighbourhood and return only this. My approach to this was to use .groupby and .idxmax() to get the maximum values w.r.t to the Index, and then iterate through
Not able to perform operations on resulting dataframe after “join” operation in PySpark
Here I have created three dataframes: df,rule_df and query_df. I’ve performed inner join on rule_df and query_df, and stored the resulting dataframe in join_df. However, when I try to simply print the columns of the join_df dataframe, I get the following error- The resultant dataframe is not behaving as one, I’m not able to perform any dataframe operations on it.
Is there any function to get multiple timeseries with .get and create a dataframe in Pandas?
I get multiple time series data in series format with datetimeindex, which I want to resample and convert to a dataframe with multiple columns with each column representing each time series. I am using separate functions to create the dataframe, for example, .get(), .resample(), pd.concat(). Since it is not following the DRY principle (Don’t Repeat Yourself) and I can be