I have a pandas dataframe with an index and jut one column. Index has Dates. Column has Values. I would like to find the NewValue of a NewDate that is not in the index. To do that i suppose i may use interpolation function as: NewValue=InterpolationFunction(NewDate,Index,Column,method ext..). So what is the InterpolationFunction? It seems that most of interpolation functions are used for padding, finding the missing values ext. This is not what i want. I just want the NewValue. Not built a new Dataframe ext..
Thank you very very much for taking the time to read this.
I am afraid that you cannot find the missing values without constructing a base for your data. here is the answer to your question if you make a base dataframe:
You need to construct a panel in order to set up your data for proper interpolation. For instance, in the case of the date, let's say that you have yearly data and you want to add information for a missing year in between or generate data for quarterly intervals.
You need to construct a base for the time series i.e.:
dates = pd.date_range(start="1987-01-01",end="2021-01-01", freq="Q").values
panel = pd.DataFrame({'date_Q' :dates})
Now you can join your data to this structure and it will have dates without information as missing. Now you need to use a proper interpolation algorithm to fill the missing values. Pandas .interpolate() method has some basic interpolation methods such as polynomial and linear which you can find here.
However, much more ways of interpolation are offered by Scipy which you can find in the tutorials here.
Related
So, I am trying to pivot this data (link here) to where all the metrics/numbers are in a column with another column being the ID column. Obviously having a ton of data/metrics in a bunch of columns is much harder to compare and do calculations off of than having it all in one column.
So, I know what tools I need for this; Pandas, Findall, Wide_to_long (or melt) and maybe stack. However, I am having a bit of difficulty putting them all in the right place.
I easily can import the data into the df and view it, but when it comes to using findall with wide_to_long to pivot the data I get pretty confused. I am basing my idea off of this example (about half way down when they use the findall / regex to define new column names). I am looking to create a new column for each category of the metrics (ie. population estimate is one column and then % change is another, they should not be all one column)
Can someone help me set up the syntax correctly for this part? I am not good at the expressions dealing with pattern recognition.
Thank you.
I have one dataframe of readings that come in a particular arrangement due to the nature of the experiment. I also have another dataframe, that contains information about each point on the dataframe and what each point corresponds to, in terms of what chemical was at that point. Note, there are only a few different chemicals over the dataframe, but they are arranged all over the dataframe.
What I want to do is to create a new, reorganised dataframe, where the columns are the type of chemical. My inital thought would be to compare the data and information dataframes to produce a dictionary, which I could then transform into a new dataframe. I could not figure out how to do this, and might not actually be the best approach either!
I have previously achieved it by manually rearranging the points on the dataframe to match the pattern I want, but I'm not happy with this approach and must be a better way.
Thanks in advance for any help!
My dataset is in an odd format and I have no clue how to fix it (I have tried and read a lot of similar questions but to no avail). Each column is a firm name (e.g. AAPL, AMZN, FB) and the first row is a list of each category of data. Basically each column has a firm name, then the entry below is a code (e.g. trading volume, market value, price), and then the respective data with an index of dates (monthly). How can I appropriately manipulate this so I can filter the data for a panel regression? Example: using each column of trading volume regressed on each column of earnings per share?
It sounds like you may need to learn how to select columns from Pandas MultiIndex, and perhaps how to create a MultiIndex. You may also benefit from learning how to reshape your data in order to run your panel regression.
If you provide a small sample of your data with the correct format, it will be easier to provide more specifics.
I am trying to decompose a Time Series, however my data does not have Dates, it is composed of entries taken at regular (and unknown) time intervals.
This solution is great and exactly what I want, however it assumed that my series has a datetime index, which it does not.
I can estimate the frequency parameter in this specific case, however this will need to be automated for different data, and as such I can not use the freq parameter of the seasonal_decompose function (unless there is some way to automatically calculate this) to make do for the fact that my series lacks a datetime index.
I have managed to estimate season lenght by utilizing the seasonal python package.
Using fit_seasons function and then seeing the lenght of the returned seasons.
In pandas you can replace the default integer-based index with an index made up of any number of columns using set_index().
What confuses me, though, is when you would want to do this. Regardless of whether the series is a column or part of the index, you can filter values in the series using boolean indexing for columns, or xs() for rows. You can sort on the columns or index using either sort_values() or sort_index().
The only real difference I've encountered is that indexes have issues when there are duplicate values, so it seems that using an index is more restrictive, if anything.
Why then, would I want to convert my columns into an index in Pandas?
In my opinion custom indexes are good for quickly selecting data.
They're also useful for aligning data for mapping, for aritmetic operations where the index is used for data alignment, for joining data, and for getting minimal or maximal rows per group.
DatetimeIndex is nice for partial string indexing, for resampling.
But you are right, a duplicate index is problematic, especially for reindexing.
Docs:
Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display
Enables automatic and explicit data alignment
Allows intuitive getting and setting of subsets of the data set
Also you can check Modern pandas - Indexes, direct link.
As of 0.20.2, some methods, such as .unstack(), only work with indices.
Custom indices, especially indexing by time, can be particularly convenient. Besides resampling and aggregating over any time interval (the latter is done using .groupby() with pd.TimeGrouper()) which require a DateTimeIndex, you can call the .plot() method on a column, e.g. df['column'].plot() and immediately get a time series plot.
The most useful though, is alignment: for example, suppose you had some two sets of data that you want to add; they're labeled consistently, but sorted in a different order. If you set their labels to be the index of their dataframe, you can simply add the dataframes together and not worry about the ordering of the data.