I am trying to insert data into a specific cell in csv. My code is as follows. The existing file. Output The data in cell A1(“Custmor”) is replaced with new data(“Name”). My code is as follows. And it doesn’t work. Please help me finding the bug. Answer Customer is column header, you need do
Tag: pandas
Pandas multiIndex slicing by aggregate
I have a pandas Series (S) that has an index like: I have a conditional function that returns a lower dimensional index. What I am doing is performing something like S.groupby(level=(0,1)).median() > 1 This returns a Series with Index like so: How do I slice the original Series with the lower dimensional index? I know I can reset index and
Why Pandas custs the zero in the first position?
I load data to dataframe: Dataframe has column with value: 00590 After load dataframe I got this as 590. I have tried dtype=’object’. Does not help me. Answer Have you tried using str instead of object? if you use str (string) it maintains the zeros at the beginning. It could be good to specify the column name you would like
how to add a dataframe with another dataframe and updated common values based on a column
my first data frame- second data frame- I want dataframe dataframe Like this (all values of df2 and uncommon values of df1)- I tried like this- but its not working fine……. Can anyone please suggest a better way- Answer Use pd.concat and drop_duplicates:
Transpose a single column in DF
The table I have The transpose I want to do is single column transpose index name value batchnumber 0 Result 1 255.00 59534 1 Result 2 260.00 59534 2 Result 3 285.00 59534 3 Result 1 255.00 59530 4 Result 2 260.00 59530 5 Result 3 325.00 59530 I tried pivot Answer You can pivot_wider then melt. In this case,
Replace decimals in floating point numbers
Someone on this platform has already helped me with generating the following code: This code ensures that the last decimal is not a 0 or a 9. However, I would like to have no 0’s nor 9’s in all the decimals that are generated (such that it will not be possible to have a number 1.963749 or 3.459007). It would
pandas convert timestamp in a dynamic way
I have a dataframe which is having the following timestamps. Total of 5600000 rows! Because I am doing the following operation, it throws an error. (TypeError: Timestamp subtraction must have the same timezones or no timezones) Here i know the problem, and it could be solved by (pd.to_datetime(“2016-01-02 11:23:04”) – pd.Timestamp(“1970-01-01”)) I am looking for an optimized solution for this
pd.read_csv() keep number of decimals
I want to read a csv but it culls the number of decimals: gives (first 2 rows, first five columns): enter image description here the original data (here) has 8 decimal places in the 3rd and 4th columns. I need those. 2211 196568.000 -25.732036008 28.282629130 1387.8994 2211 196569.000 -25.732032386 28.282633712 1389.4025 How do I read a csv and retain the
What is the syntax for removing duplicate rows in pandas (where all of the columns match)? [closed]
Closed. This question needs debugging details. It is not currently accepting answers. Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question. Closed 10 months ago. Improve this question What is the syntax for removing duplicate rows of a pandas dataframe (where
Select colums in pandas multi index dataframe
I probably have a rather simple pandas question, but despite having tried multiple solutions posted on stackoverflow, I can’t figure out how to do it properly. I have pandas multi-index Dataframe with the following structure: Image1 now I want to select a subset of this dataframe based on the first column headers (HDx_DATE) The columns which I want to show