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’, …
Replace missing values with the value of the column with the minimum sum of differences Question: I have the dataframe below. # Create a sample DataFrame df = pd.DataFrame({‘Age’: [np.nan, 31, 29, 43, np.nan], ‘Weight’: [np.nan, 100, 60, 75, np.nan], ‘Height’: [1.65, 1.64, 1.75, 1.70, 1.68], ‘BMI’: [19, 15, 10, 25, 30]}) and the columns …
ImportError: cannot import name '_check_weights' from 'sklearn.neighbors._base' Question: I am trying to do Missforest as a method for handling missing values in table data. import sklearn print(sklearn.__version__) ->1.2.1 import sklearn.neighbors._base import sys sys.modules[‘sklearn.neighbors.base’] = sklearn.neighbors._base !pip install missingpy from missingpy import MissForest It was working fine until now, but since yesterday, the following error message …
How do you fill missing dates in a Polars dataframe (python)? Question: I do not seem to find an equivalent for Polars library. But basically, what I want to do is fill missing dates between two dates for a big dataframe. It has to be Polars because of the size of the data (> 100 …
How can we make pandas default handling of missing values warn of their presence rather than silently ignore them? Question: As discussed here, pandas silently replaces NaN values with 0 when calculating sums, in contrast to explicit calculations as shown here: import pandas as pd import numpy as np np.NaN + np.NaN # Result: nan …
time series stock data having gaps in dataframe to be modeled in Pycaret Question: I have a a csv file which I have imported as follows: ps0pyc=pd.read_csv(r’/Users/swapnilgupta/Desktop/fend/p0.csv’) ps0pyc[‘Date’] = pd.to_datetime(ps0pyc[‘Date’], dayfirst= True) ps0pyc Date PORTVAL 0 2013-01-03 17.133585 1 2013-01-04 17.130434 2 2013-01-07 17.396581 3 2013-01-08 17.308323 4 2013-01-09 17.475933 … … … 2262 2021-12-28 …
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 | …
pandas.read_excel() na_values not working correctly Question: As title states, after reviewing docs I am reading an .xlsx file, with a column ‘HOUR’ which has many values, when an instance has value 99, i want to convert to None I have tried the na_values param with different values: na_values = [’99’] na_values = [r’99’] na_values = …
TypeError: cannot subtract DatetimeArray from ndarray when using time stamp data Question: I am trying to calculate the number of days between two columns where each column stored as a TimeStamp object and contain NaN values. When I try to make the calculation, I am receiving TypeError: cannot subtract DatetimeArray from ndarray error. My question …
Faster way to find all columns are with no missing values? Question: Currently I am using this statement to find all columns in a dataframe that has no missing values, it works fine. but I’m wondering if there is more concise way (albeit, efficient way) to do the same thing? df.columns[ np.sum(df.isnull()) == 0 ] …
