So I'm very new to python and I'm using Pandas to read an excel file, my file column is having 197 values to it, so when I read them with Pandas, I don't get all of the values " as shown in the picture"
not the full excel sheet is appearing
import pandas as pd
xl =pd.ExcelFile('test.xlsx')
sheet1 = xl.parse()
z=str(sheet1)
z=z.replace('212/',"")
z=z.replace('/1',"")
print(z)
Thanks for helping.
Is your question to show those values? What you see is normal behavior. If you want see specific rows, try loc or iloc.
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'm just wondering how to update a single cell in an excel spreadsheet with Pandas in a python script. I don't want any of the other cells in the file to be overwritten, just the one cell I'm trying to update. I tried using .at[], .iat[], and .loc() but my excel spreadsheet does not update. None of the other deprecated methods like .set_value() work either. What am I doing wrong?
import pandas as pd
tp = pd.read_excel("testbook.xlsx", sheet_name = "Sheet1")
tp.at[1, 'A'] = 10
I might suggest using xlwings for this operation, as it might be easier than reading and writing a sheet in pandas dataframes. The example below changes the value of "A1".
import xlwings as xw
sheet = xw.Book("testbook.xlsx").sheets("Sheet1")
sheet.range("A1").value = "hello world"
Also note xlwings is included with all Anaconda packages if you're using that: https://docs.xlwings.org/en/stable/api.html
I am trying to open a csv file using pandas.
This is a screenshot of the file opened in excel.
Some columns have names and some do not. When trying to read this in with pandas I get the "ValueError: Passed header names mismatches usecols" error.
When I open part of the file in excel, add column names, save, and then import with pandas it works.
The problem is the files are large and cannot fully open in excel (plus I'd prefer a more elegant solution anyway).
Is there a way to deal with this issue in pandas?
I have read answers to other questions regarding this error but none were relevant.
Thanks so much in advance!
In names you can provide column names:
df = pd.read_csv('pandas_dataframe_importing_csv/example.csv', names=['col1', 'col2', 'col3'], engine='python')
I would like to delete some rows in my Excel's file with Python.
File
In fact the interesting part for my use begins Row 6 with "30". I'm looking for a good way to delete the rows that are above.
How should I do that?
Thanks.
If you're not opposed to use Pandas, then try this:
import pandas as pd
table = pd.read_excel("file.xlsx", skiprows=list(range(5)))
Here using skiprows will not read in the first 5 rows, then if you want to save it as an excel file:
table.to_excel("new_file.xlsx")
I am new to Pandas for Python and am busy reading a csv file. Unfortunately the Excel file has some cells with #VALUE! and #DIV/0! in them. I cannot fix this in Excel because the data is pulled from other sheets. Pandas turns these columns into objects instead of numpy64, so I cannot plot from them. I want to replace the #VALUE! and #DIV/0! strings with NaN entries in Pandas, however i cannot find how to do this. I have tried the following (my code runs, but it changes nothing):
import pandas as pd
import numpy as np
df = pd.read_csv('2013AllData.csv')
df.replace('#DIV/0!', np.nan)
Rather than replacing after loading, just set the param na_values when reading the csv in and it will convert them to NaN values when the df is created:
df = pd.read_csv('2013AllData.csv', na_values=['#VALUE!', '#DIV/0!'])
Check the docs: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html#pandas.read_csv