I have a text file that contains data like this. It is is just a small example, but the real one is pretty similar.
I am wondering how to display such data in an "Excel Table" like this using Python?
The pandas library is wonderful for reading csv files (which is the file content in the image you linked). You can read in a csv or a txt file using the pandas library and output this to excel in 3 simple lines.
import pandas as pd
df = pd.read_csv('input.csv') # if your file is comma separated
or if your file is tab delimited '\t':
df = pd.read_csv('input.csv', sep='\t')
To save to excel file add the following:
df.to_excel('output.xlsx', 'Sheet1')
complete code:
import pandas as pd
df = pd.read_csv('input.csv') # can replace with df = pd.read_table('input.txt') for '\t'
df.to_excel('output.xlsx', 'Sheet1')
This will explicitly keep the index, so if your input file was:
A,B,C
1,2,3
4,5,6
7,8,9
Your output excel would look like this:
You can see your data has been shifted one column and your index axis has been kept. If you do not want this index column (because you have not assigned your df an index so it has the arbitrary one provided by pandas):
df.to_excel('output.xlsx', 'Sheet1', index=False)
Your output will look like:
Here you can see the index has been dropped from the excel file.
You do not need python! Just rename your text file to CSV and voila, you get your desired output :)
If you want to rename using python then -
You can use os.rename function
os.rename(src, dst)
Where src is the source file and dst is the destination file
XLWT
I use the XLWT library. It produces native Excel files, which is much better than simply importing text files as CSV files. It is a bit of work, but provides most key Excel features, including setting column widths, cell colors, cell formatting, etc.
saving this is:
df.to_excel("testfile.xlsx")
Related
So, I am actually handling text responses from surveys, and it is common to have responses that starts with -, an example is: -I am sad today.
Excel would interpret it as #NAMES?
So when I import the excel file into pandas using read_excel, it would show NAN.
Now is there any method to force excel to retain as raw strings instead interpret it at formula level?
I created a vba and assigning the entire column with text to click through all the cells in the column, which is slow if there is ten thousand++ data.
I was hoping it can do it at python level instead, any idea?
I hope, it works for your solution, use openpyxl to extract excel data and then convert it into a pandas dataframe
from openpyxl import load_workbook
import pandas as pd
wb = load_workbook(filename = './formula_contains_raw.xlsx', ).active
print(wb.values)
# sheet_names = wb.get_sheet_names()[0]
# sheet_ranges = wb[name]
df = pd.DataFrame(list(wb.values)[1:], columns=list(wb.values)[0])
df.head()
It works for me using a CSV instead of excel file.
In the CSV file (opened in excel) I need to select the option Formulas/Show Formulas, then save the file.
pd.read_csv('draft.csv')
Output:
Col1
0 hello
1 =-hello
I have an excel file that contains the names of 60 datasets.
I'm trying to write a piece of code that "enters" the Excel file, accesses a specific dataset (whose name is in the Excel file), gathers and analyses some data and finally, creates a new column in the Excel file and inserts the information gathered beforehand.
I can do most of it, except for the part of adding a new column and entering the data.
I was trying to do something like this:
path_data = **the path to the excel file**
recap = pd.read_excel(os.path.join(path_data,'My_Excel.xlsx')) # where I access the Excel file
recap['New information Column'] = Some Value
Is this a correct way of doing this? And if so, can someone suggest a better way (that works ehehe)
Thank you a lot!
You can import the excel file into python using pandas.
import pandas as pd
df = pd.read_excel (r'Path\Filename.xlsx')
print (df)
If you have many sheets, then you could do this:
import pandas as pd
df = pd.read_excel (r'Path\Filename.xlsx', sheet_name='sheetname')
print (df)
To add a new column you could do the following:
df['name of the new column'] = 'things to add'
Then when you're ready, you can export it as xlsx:
import openpyxl
# to excel
df.to_excel(r'Path\filename.xlsx')
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)
I have around 60 .csv files which i would like to combine in pandas. So far i've used this:
import pandas as pd
import glob
total_files = glob.glob("something*.csv")
data = []
for csv in total_files:
list = pd.read_csv(csv, encoding="utf-8", sep='delimiter', engine='python')
data.append(list)
biggerlist = pd.concat(data, ignore_index=True)
biggerlist.to_csv("output.csv")
This works somewhat, only the files I would like to combine all have the same structure of 15 columns with the same headers. When I use this code, only one column is filled with info of the entire row, and every column name is add-up of all column names (e.g. SEARCH_ROW, DATE, TEXT, etc.).
How can I combine these csv files, while keeping the same structure of the original files?
Edit:
So perhaps I should be a bit more specific regarding my data. This is a snapshot of one of the .csv files i'm using:
As you can see it is just newspaper-data, where the last column is 'TEXT', which isn't shown completely when you open the file.
This is a part of how it looks when i have combined the data using my code.
Apart, i can read any of these .csv files no problem using
data = pd.read_csv("something.csv",encoding="utf-8", sep='delimiter', engine='python')
I solved it!
The problem was the amount of comma's in the text part of my .csv files. So after removing all comma's (just using search/replace), I used:
import pandas
import glob
filenames = glob.glob("something*.csv")
df = pandas.DataFrame()
for filename in filenames:
df = df.append(pandas.read_csv(filename, encoding="utf-8", sep=";"))
Thanks for all the help.
I read an Excel Sheet this way :
import pandas as pd
xl = pd.ExcelFile("Path\file_name.xlsx")
xl.parse("Sheet_name")
and now I make some changes, for example I fill all null values with string "NA"
df = df.fillna("NA")
now I wish to write back the changes to the original Excel file...
You could simply write the changed data to the existing file using to_excel:
df.to_excel("Path\file_name.xlsx", "Sheet_name")
I would work with CSV files if you have large data sets.
save excel as .csv
data = pd.read_csv('name.csv')
data.to_csv('name.csv')