I have a dataset that I need to change the NaNs with a value when I do so there is no value stored. the data looks like this: id type status job_id 1 EMP Pending 1 EMP Pending 101 1 Contract Aproved 391 2 EMP Approved 521 2 Contract Approved This is the code I… Read More Trying to Sort by Values with NaN
I have a dataframe that looks something like this: ID hiqual Wave 1 1.0 g 1 NaN i 1 NaN k 2 1.0 g 2 NaN i 2 NaN k 3 1.0 g 3 NaN i 4 5.0 g 4 NaN i This is a long format dataframe and I have my hiqual variable for… Read More How to populate NaN values based on conditions from two other columns using Pandas?
Why do we replace the nan value in DataFrame with the Mean, and when we change it doesn’t it affect our data ? 0 1.048242 1 1.688173 2 NaN 3 0.194162 4 0.194162 5 0.493194 6 NaN 7 0.675041 8 NaN 9 0.101743 10 3.112086 df[‘view_duration’].fillna(mean,inplace = True) 0 1.048242 1 1.688173 2 0.938350 3… Read More Imputation: Why do we replace the nan value with the Mean, and doesn't it affect our data?
I have a dataframe like as below df = pd.DataFrame({‘subject_id’:[1,1,1,1,1,2,2,2,2,2], ‘qtr_info’ :[‘2017Q1′,’2017Q3′,’2017Q4′,’2018Q1′,’2018Q4′,’2017Q1′,’2017Q4′,’2018Q2′,’2018Q4′,’2019Q1’], ‘val’ :[5,5,5,5,1,6,5,5,8,3], ‘Prod_id’:[‘A’,’B’,’C’,’A’,’E’,’Q’,’G’,’F’,’G’,’H’]}) I would like to do the below a) Fill all the missing quarters of a subject b) fillna for other columns using the mean value for respective columns (for the same subject). Don’t refer other subject ids for computing mean… Read More Pandas resample based on string like PeriodIndex
I’m working with a time-series dataset whose my interest columns are the month column (mes), the year column (ano), the minimum prices (min) and the modal price (mcom). some months from the year 2013 doesn’t have any value in the min column, and in those months and want to fill with the values that are… Read More Fill nan values from a column with values from another column
What I want here is for the Nans to ultimately be integer values. Since my dataset is 1000s of columns, I can’t just change a couple of columns to make them integer and when I tried df = df.astype(‘int’) in Dask, after changing the values to 0 floats, for whatever reason, it didn’t work. `… Read More Filling NAs to string 0 and then to int reverts to float
I have a dataframe like as below id,status,amount,qty 1,pass,123,4500 1,pass,156,3210 1,fail,687,2137 1,fail,456,1236 2,pass,216,324 2,pass,678,241 2,nan,637,213 2,pass,213,543 df = pd.read_clipboard(sep=’,’) I would like to do the below a) Groupby id and compute the pass percentage for each id b) Groupby id and compute the average amount for each id So, I tried the below df[‘amt_avg’] =… Read More Pandas groupby and compute ratio of values with NA in multiple columns
I have a dataframe and dictionary like as shown below ID,Name,value,total, 1,Ajay,2.00,35 1,Dan,3.00,65 2,Ajay,2,78 2,Rajini,0.0,98 3,Ajay,3.00,53 3,Rad,75.25,21 df1 = pd.read_clipboard(sep=’,’) output = {‘Ajay’: {1: ‘ABC’, 2: ‘DEF’, 3: ‘DUMMA’, 4: ‘CHUMMA’}, ‘Dan’: {0: ‘KOREA’, 1: ‘AUS/NZ’, 2: ‘INDIA’, 3: ‘ASEAN’}} I would like to do the below a) Replace the values in value column by… Read More pandas replace dataframe float values using int keys of nested dict
for example, I have the next list: l = [‘a’, ‘x’, ‘t’] and the DataFrame: a = [{‘sufix’: ‘a’, ‘qty’: 5}, {‘sufix’: ‘b’, ‘qty’: 2}, {‘sufix’: ‘c’, ‘qty’: 7}, {‘sufix’: ‘x’, ‘qty’: 9}, {‘sufix’: ‘t’, ‘qty’: 4}, {‘sufix’: ‘p’, ‘qty’: 1}] df = pd.DataFrame(a) print(df) What I need, if values from list -> l are… Read More How to check values from list in DataFrame column and insert value by condition?
I try to calculate for each element in a Dataframe df1 the cumulative product over x days (for example 3). Is there an efficient way to do that? So far, I only know to generate a normal cumulative product over the rows, without fixing the lookback period to x days (=df2). For example: 2022-01-02 ID1:… Read More Cumulative product for each element over x days in pandas DataFrame