I need help about read csv file with pandas.
I have a .csv file that recorded machine parameters and want to read this excel with pandas and analyze. But problem is this excel file not in a proper table format. That means there are a lot of empty rows and columns. Also parameter values are starting from 301st line (example).
How can I read as properly this csv file?
You can use skiprows:
pd.read_csv(csv_file, skiprows=301)
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html
Related
Using Pandas, I'm trying to read an excel file that looks like the following:
another sample
I tried to read the excel file using the regular approach by running: df = pd.read_excel('filename.xlsx', skiprows=6).
But the problem with it is that I don't get all the columns names needed and most of the column names are Unnamed:1
Is there a way to solve these and read all the columns? Or an approach were I can convert it to a json file
I initially made an empty excel file with column names (5 columns in each sheet) and sheet names (4 sheets with names).
When I tried to write data (a scalar value at a time, say 5) in an excel sheet using ExcelWriter, to_excel in Pandas. It deletes the previous data as well as deletes other sheets.
I don't want to aggregate the data in a variable and write it at once. Because this is a part of a time-consuming experiment and I want to save data regularly.
If the same can be done with normal python (without pandas), kindly suggest.
From pandas documentation, you need to create an ExcelWriter which opens the Excel file in append mode:
with ExcelWriter('path_to_file.xlsx', mode='a') as writer:
df.to_excel(writer, sheet_name='sheet_name')
Have an excel file with a column with some text in each row of this column.
I'm using pandas pd.read_excel() to open this .xlsx file. Then I would like to do the following: I would like to save every row of this column as a distinct .txt file (that would have the text from this row inside this file). Is it possible to be done via pandas?
the basic idea would be to use an iterator to loop over the rows, opening a each file and writing the value in, something like:
import pandas as pd
df = pd.read_excel('test.xlsx')
for i, value in enumerate(df['column']):
with open(f'row-{i}.txt', 'w') as fd:
fd.write(value)
In python, how can I read an unstructured csv file (with some redundant rows of texts) and output it as a new structured csv file using pandas?
There are some unwanted rows in the csv file (at the very beginning as shown by the picture) which is getting parsed as a unique column resulting in incorrect format of columns, but actually these lines should be ignored
The unstructured csv
The Desired Structure :
I have searched for a solution, but none of the previous questions here solve my problem
I have an excel sheet and I am reading the excel sheet using pandas in python.
Now I want to read the excel file based on a column, if the column has some value then do not read that row, if the column is empty than read that and store the values in a list.
Here is a screenshot
Excel Example
Now in the above image when the uniqueidentifier is yes then it should not read that value, but if it is empty then it should start reading from that value.
How to do that using python and how to get index so that after I have performed some function that I am again able to write to that blank unique identifier column saying that row has been read
This is possible for csv files. There you could do
iter_csv = pandas.read_csv('file.csv', iterator=True, chunksize=100000)
df = pd.concat([chunk[chunk['UniqueIdentifier'] == 'True'] for chunk in iter_csv])
But pd.read_excel does not offer to return an iterator object, maybe some other excel-readers can. But I don't no which ones. Nevertheless you could export your excel file as csv and use the solution for csv files.