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Dec 13, 2012 · To directly answer this question's original title "How to delete rows from a pandas DataFrame based on a conditional expression" (which I understand is not necessarily the OP's problem but could help other users coming across this question) one way to do this is to use the drop method:
Nov 16, 2012 · We can remove or delete a specified column or specified columns by the drop () method. Suppose df is a dataframe. Column to be removed = column0. Code: df = df.drop(column0, axis=1) To remove multiple columns col1, col2, . . . , coln, we have to insert all the columns that needed to be removed in a list.
Mar 31, 2017 · you can just use : df.drop([a,b,c]) where a,b,c are the list of indexes or row numbers. to delete only one particular row use. df.drop(i) where i is the index or the row number. edited May 24, 2020 at 21:05. David Buck. 3,784 35 33 37.
Jun 20, 2022 · To remove both Nan, and inf using a single command use. df = df[ np.isfinite( df ).all( axis = 1) ] If for some reason the above doesn't work for you, please try the following 2 steps: df = df[ ~( df.isnull().any( axis = 1 ) ) ] #to remove nan. df = df[ ~( df.isin( [np.inf, -np.inf]).any(axis =1) )] #to remove inf.
Jun 16, 2018 · Use drop_duplicates() by using column name. import pandas as pd data = pd.read_excel('your_excel_path_goes_here.xlsx') #print(data) data.drop_duplicates(subset=["Column1"], keep="first") keep=first to instruct Python to keep the first value and remove other columns duplicate values.
The pandas drop_duplicates function is great for "uniquifying" a dataframe. I would like to drop all rows which are duplicates across a subset of columns. Is this possible? A B C 0 foo 0 A 1 foo 1 A 2 foo 1 B 3 bar 1 A
Jun 21, 2019 · To fix this, you can convert the empty stings (or whatever is in your empty cells) to np.nan objects using replace(), and then call dropna() on your DataFrame to delete rows with null tenants. To demonstrate, we create a DataFrame with some random values and some empty strings in a Tenants column: >>> import pandas as pd. >>> import numpy as np.
I have a very large data frame in python and I want to drop all rows that have a particular string inside a particular column. For example, I want to drop all rows which have the string "XYZ" as a substring in the column C of the data frame. Can this be implemented in an efficient way using .drop() method?
One can use drop DataFrame.drop for that. Considering that one wants to drop the rows, one should use axis=0 or axis='index'. If one wants to drop columns, axis=1 or axis='columns'. For your specific case, one can do. wrong_indexes_train = [0, 63, 151, 469, 1008]
Drop or filter columns in pandas dataframe-1. Dropping pandas dataframe columns. Related. 76. Keep certain ...
