Filling DF's NaN/Missing data from another DF Question: I have two data frames: df1 = pd.DataFrame({‘Group’: [‘xx’, ‘yy’, ‘zz’, ‘x’, ‘x’, ‘x’,’z’,’y’,’y’,’y’,’y’], ‘Name’: [‘A’, ‘B’, ‘C’, None, None, None, None, None, None, None, None], ‘Value’: [5, 3, 4, 7, 1, 3, 6, 5, 9, 5, 4]}) df2 = pd.DataFrame({‘Name’: [‘A’, ‘A’, ‘B’, ‘B’], ‘Group’: [‘x’, …
Python Pandas ffill or bffill with multiple condition Question: I have a dataset like below : condition=[None,None,None,’condtion1′,None,None,None,’conditon2′,None,None,None] add_val = [None,None,None,10,None,None,None,20,None,None,None] df=pd.DataFrame({‘event_condition’:condition,’add_col’:add_val}) I want to fillna of add_col in terms of condition. When I meet condition 1 of event_condition, I want to ffill with value of 10 in a separate column called ‘on_condition1’. It should not …
pandas, how only fillna for last row with preceding line closest non-nan value Question: import pandas as pd import numpy as np df = pd.DataFrame(np.random.randint(0, 10, (10, 10)), columns=list(‘ABCDEFGHIJ’)) df[df > 5] = np.nan for i in range(10): df.iloc[i, i % 10] = np.nan print(df) origin df is: A B C D E F G …
Trying to Sort by Values with NaN Question: 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 …
How to populate NaN values based on conditions from two other columns using Pandas? Question: 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 …
How can I replace a nan value in an "object" column? Question: How can I replace a nan value in a column which is an ‘object’, contains other str and ensure it goes into the main df and not just the slice. I have tried this covid_19_df.Country_code.fillna(‘OT’) but it is still empty D Country_code Country …
Filling null values based on the proportion of the categories in that column Question: I have the following data col=[‘black’,’black’,’black’,’grey’,’white’,’grey’,’grey’,’nan’,’grey’,’black’,’black’,’red’,’nan’,’nan’,’nan’,’nan’,’black’,’black’,’white’] dd=pd.DataFrame({‘color’:col}) dd.replace(‘nan’,np.NaN,inplace=True) dd.sample(5) Out[1]: color 8 grey 14 NaN 7 NaN 2 black 9 black The following is the proportion of each color in the column dd.color.value_counts(normalize=True) Out[2]: black 0.500000 grey 0.285714 white 0.142857 red …
Imputation: Why do we replace the nan value with the Mean, and doesn't it affect our data? Question: 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 …
How to fill NaN only in numeric variables if that variable in on list in Python Pandas? Question: I have Pandas DataFrame like below: data types: COL1 – numeric COL2 – object COL3 – numeric TABLE 1 COL1 | COL2 | COL3 —–|——|—— 123 | AAA | 99 NaN | ABC | 1 111 | …
How to backfill values based on a category pandas Question: I have a table such as the following, with vaccine manufacturer, date and total_vaccinations as a cumulative sum of the vaccine manufacturer over time I am trying to ‘backfill’ the nulls, basically such that since this is a cumulative sum, dates with null values would …
