How do I replace a specific value in Pandas?

How do I replace values in a CSV file in Pandas Python?

I have this .

Csv: name, address. A, B. C, D. E, F. G, H. And I want to write a .py function that given my input will replace the "name" column with "NewName" in the above file.

With open('file.csv') as infile: with open('updatedfile.csv', 'w') as outfile: # iterate over the rows in the data. # find the row that needs to be updated. # update the correct row in the data. # Write out the new row to file. You need to iterate over the data somehow, you can do that by using something like this: reader = csv.

How do I replace data in a CSV file?

I'm reading in a csv file, and I have some columns that contain bad data, so I want to replace that data with NI've found many ways to change the data, but I want to make sure I'm using the correct method.

In particular, I'm confused about if there's a way to change the data before opening the file. I know how to read the data in, and change it, but what is the best method to replace the bad data?
My first idea was to simply read in the file, replace the bad data, and write it out, but I was told that's not a good way to do things in python. It looks like the only way to change the data before writing the file would be to overwrite the whole file. Is there a better way?
Also, should I create a new file, read the data in, modify it, and write it to a new file? Or should I create a new dataframe, replace the data, and overwrite the dataframe? Read the original data into a dataframe with replace() (as per your question). This is a common pattern in data processing when you have data with bad values. Instead of having a bunch of columns for each data type, replace all data that is "bad" with a specific value, which in this case will be NaN.

For example: import pandas as pd. Import numpy as np. Df = pd.DataFrame() df.replace('NA', np.nan)

Output: foo bar blah. 0 1 4.0 7.0
1 2 5.0 8.0
2 3 6.0 9.