I have a dataframe with 5 columns (Participants, duration_1, duration_2, duration_3, duration_4). The "Participant" column has either subjects with the IDCY or IDCO labels. FOr example: IDCY06, IDCO02,IDCY31...etc. I want to create two new dataframes: those with the IDCY and those with IDCO. I have been using the code:
df[df["Participant"].str.contains("IDCY")]
and I keep getting a keyerror code for Participants even though everything is spelled as it should be.
Is there any other method to iterate over rows and to get a new dataframe with the participants that have a set substring?
Thank you.
Related
I cant seem to find a way to split all of the array values from the column of a dataframe.
I have managed to get all the array values using this code:
The dataframe is as follows:
I want to use value.counts() on the dataframe and I get this
I want the array values that are clubbed together to be split so that I can get the accurate count of every value.
Thanks in advance!
You could try .explode(), which would create a new row for every value in each list.
df_mentioned_id_exploded = pd.DataFrame(df_mentioned_id.explode('entities.user_mentions'))
With the above code you would create a new dataframe df_mentioned_id_exploded with a single column entities.user_mentions, which you could then use .value_counts() on.
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()
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 working on a dataframe with where I have multiple columns and in one of the columns where there are many rows approx more than 1000 rows which contains the string values. Kindly check the below table for more details:
In the above image I want to change the string values in the column Group_Number to number by picking the values from the first column (MasterGroup) and increment by one (01) and want values to be like below:
Also need to verify that if the String is duplicating then instead of giving a new number it replaces with already changed number. For example in the above image ANAYSIM is duplicating and instead of giving a new sequence number I want already given number to repeating string.
Have checked different links but they are focusing on giving values from user:
Pandas DataFrame: replace all values in a column, based on condition
Change one value based on another value in pandas
Conditional Replace Pandas
Any help with achieving the desired outcome is highly appreciated.
We could do cumcount with groupby
s=(df.groupby('MasterGroup').cumcount()+1).mul(10).astype(str)
t=pd.to_datetime(df.Group_number, errors='coerce')
Then we assign
df.loc[t.isnull(), 'Group_number']=df.MasterGroup.astype(str)+s
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