I am new to pandas. And really confused with working on a dataframe with multi-level index for columns. I want to re-name my level 2 column name by appending with the column.iloc: _0, _1, … add a new column New_Max which is the max value of the previous 2 columns. The level 0 & 1 name for New_Max is not
Tag: multi-index
How to Efficiently Perform Multiplication within MultiIndex Groupby
I am trying to use two of my second level indices to calculate a third index. However, I can’t find an idiomatic way to do this. How can I calculate one second level index from two other second level indices? Each group has the same second level indices. My Code This produces the following data frame: What I Have Note
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
Converting a multindex dataframe to a nested dictionary [closed]
Closed. This question needs debugging details. It is not currently accepting answers. Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question. Closed 2 years ago. Improve this question I have a grouped dataframe as shown in this link: I want to
Python : get access to a column of a dataframe with multiindex
Let’s say that I have this dataframe with multi index : How do we get access to the data in the columns “Balance” or “Date”, I do not get why that does not work : or Answer You should use Index.get_level_values: You can pass labels : OR: Pass indices:
Problems with Multiindex from pd.Crosstab when appending non present row data
Morning All, Happy Friday. I have some excel output which shows Client, their Sector and the counts of their Result via a crosstab command. This works well: The excel extract meets the requirements. An additional request has come in to add all other possible clients which are not present in the current months data, but may be in futures months.
Drop Non-equivalent Multiindex Rows in Pandas Dataframe
Goal If sub-column min equals to sub-column max and if min and max sub-column do not equal to each other in any of the column (ao, his, cyp1a2s, cyp3a4s in this case), drop the row. Example Want Attempt Note The actual dataframe has 50+ columns. Answer Use DataFrame.xs for DataFrame by second levels of MultiIndex, replace NaNs: Or convert data
Join/Merge two Pandas dataframes and use columns as multiindex
I have two dataframes with KPIs by date. I want to combine them and use multi-index so that each KPI can be easily compared to the other for the two df. Like this: I have tried to extract each KPI into a series, rename the series accordingly (df1, df2), and then concatenating them using the keys argument of pd.concat but
applymap() does not work on Pandas MultiIndex Slice
I have an hierarchical dataset: I want to apply a function to all values under the columns A. I can set the value to something: Easy enough. Now, instead of assigning a value, if I want to apply a mapping to this MultiIndex slice, it does not work. For example, let me apply a simple formatting statement: This step works