I have a dataframe that I created from a master table in SQL. That new dataframe is then grouped by type as I want to find the outliers for each group in the master table.
The function finds the outliers, showing where in the GroupDF they outliers occur. How do I see this outliers as a part of the original dataframe? Not just volume but also location, SKU, group etc.
dataframe: HOSIERY_df
Code:
##Sku Group Data Frames
grouped_skus = sku_volume.groupby('SKUGROUP')
HOSIERY_df = grouped_skus.get_group('HOSIERY')
hosiery_outliers = find_outliers_IQR(HOSIERY_df['VOLUME'])
hosiery_outliers
#.iloc[[hosiery_outliers]]
#hosiery_outliers
Picture to show code and output:
I know enough that I need to find the rows based on location of the index. Like Vlookup in Excel but i need to do it with in Python. Not sure how to pull only the 5, 6, 7...3888 and 4482nd place in the HOSIERY_df.
You can provide a list of index numbers as integers to iloc, which it looks like you have tried based on your commented-out code. So, you may want to make sure that find_outliers_IQR is returning a list of int so it will work properly with iloc, or convert it's output.
It looks like it's currently returning a DataFrame. You can get the index of that frame as a list like this:
hosiery_outliers.index.tolist()
Related
Ive attempted to search the forum for this question, but, I believe I may not be asking it correctly. So here it goes.
I have a large data set with many columns. Originally, I needed to sum all columns for each row by multiple groups based on a name pattern of variables. I was able to do so via:
cols = data.filter(regex=r'_name$').columns
data['sum'] = data.groupby(['id','group'],as_index=False)[cols].sum().assign(sum = lambda x: x.sum(axis=1))
By running this code, I receive a modified dataframe grouped by my 2 factor variables (group & id), with all the columns, and the final sum column I need. However, now, I want to return the final sum column back into the original dataframe. The above code returns the entire modified dataframe into my sum column. I know this is achievable in R by simply adding a .$sum at the end of a piped code. Any ideas on how to get this in pandas?
My hopeful output is just a the addition of the final "sum" variable from the above lines of code into my original dataframe.
Edit: To clarify, the code above returns this entire dataframe:
All I want returned is the column in yellow
is this what you need?
data['sum'] = data.groupby(['id','group'])[cols].transform('sum').sum(axis = 1)
A few rows of my dataframe
The third column shows the time of completion of my data. Ideally, I'd want the second row to just show the date, removing the second half of the elements, but I'm not sure how to change the elements. I was able to change the (second) column of strings into a column of floats without the pound symbol in order to find the sum of costs. However, this column has no specific keyword I just select for all of the elements to remove.
Second part of my question is is it is possible to easy create another dataframe that contains 2021-05-xx or 2021-06-xx. I know there's a way to make another dataframe selecting certain rows like the top 15 or bottom 7. But I don't know if there's a way to make a dataframe finding what I mentioned. I'm thinking it follows the Series.str.contains(), but it seems like when I put '2021-05' in the (), it shows a entire dataframe of False's.
Extracting just the date and ignoring the time from the datetime column can be done by changing the formatting of the column.
df['date'] = pd.to_datetime(df['date']).dt.date
To the second part of the question about creating a new dataframe that is filtered down to only contain rows between 2021-05-xx and 2021-06-xx, we can use pandas filtering.
df_filtered = df[(df['date'] >= pd.to_datetime('2021-05-01')) & (df['date'] <= pd.to_datetime('2021-06-30'))]
Here we take advantage of two things: 1) Pandas making it easy to compare the chronology of different dates using numeric operators. 2) Us knowing that any date that contains 2021-05-xx or 2021-06-xx must come on/after the first day of May and on/before the last day of June.
There are also a few GUI's that make it easy to change the formatting of columns and to filter data without actually having to write the code yourself. I'm the creator of one of these tools, Mito. To filter dates in Mito, you can just enter the dates using our calendar input fields and Mito will generate the equivalent pandas code for you!
Apologies if this is contained in a previous answer but I've read this one: How to select rows from a DataFrame based on column values? and can't work out how to do what I need to do:
Suppose have some pandas dataframe X and one of the columns is 'timestamp'. The entries are formatted like '2010-11-03 09:44:05'. I want to select just those rows that correspond to a specific day, for example, select just those rows for which the actual string in timestamp column starts with '2010-11-03'. Is there a neat way to do this? Can I do it with a mask or Boolean indexing? Or should I just write a separate line to peel off the day from each entry and then select the rows? Bear in mind the dataframe is large if it helps.
i.e. I want to write something like
X.loc[X['timestamp'].startswith('2010-11-03')]
or
mask = '2010-11-03' in X["timestamp"]
but these don't actually make any sense.
This should work:-
X[X['timestamp'].str.startswith('2010-11-03')]
I am currently working with dataframes in pandas. In sum, I have a dataframe called "Claims" filled with customer claims data, and I want to parse all the rows in the dataframe based on the unique values found in the field 'Part ID.' I would then like to take each set of rows and append it one at a time to an empty dataframe called "emptydf." This dataframe has the same column headings as the "Claims" dataframe. Since the values in the 'Part ID' column change from week to week, I would like to find some way to do this dynamically, rather than comb through the dataframe each week manually. I was thinking of somehow incorporating the df.where() expression and a For Loop, but am at a loss as to how to put it all together. Any insight into how to go about this, or even some better methods, would be great! The code I have thus far is divided into two steps as follows:
emptydf = Claims[0:0]
#Create empty dataframe
2.Parse_Claims = Claims.query('Part_ID == 1009')
emptydf = emptydf.append(Parse_Claims)
#Parse the dataframe by each unique Part ID number and append to empty dataframe. As you can see, I can only hard code one Part ID number at a time so far. This would take hours to complete manually, so I would love to figure out a way to iterate through the Part ID column and append the data dynamically.
Needless to say, I am super new to Python, so I definitely appreciate your patience in advance!
empty_df = list(Claims.groupby(Claims['Part_ID']))
this will create a list of tuples one for each part id. each tuple has 2 elements 1st is part id and 2nd is subset for that part id
I would like to preserve the order of my DataFrame when using the .size() function. My first DataFrame is created by choosing a subset of a larger one:
df_South = df[df['REGION_NAME'] == 'South']
Here is an example of what the DataFrame looks like:
With this DataFrame I count the occurrences of each unique 'TEMPBIN_CONS' variable.
South_Count = df_South.groupby('TEMPBIN_CONS').size()
I would like to maintain the order that exists using the SORT column. I created this column based on the order I would like my 'TEMPBIN_CONS' variable to appear after counting. I can't seem to get it to appear in the proper order though. I've tried using .sort_index() on South_Count and it does not change order that groupby() creates.
Ultimately this is my solution for fixing the axis ordering of a bar plot I am creating of South_Count. As it is the ordering is very difficult to read and would like it to appear in a logical order.
For reference South_Count, and subsequently the axis of my bar plot appears in
this order:
Try this:
South_Count = df_South.groupby('TEMPBIN_CONS', sort=False ).size()
Looks as though your data is sorted as string.