I am writing a python code for beam sizing. I have an Excel workbook from AISC that has all the data of the shapes and other various information on the cross-sections. I would like to be able to reference data in particular cells in this Excel workbook in my python code.
For example if the width of rectangle is 2in and stored in cell A1 and the height is 10in and stored in cell B1 I want to write a code in python that somehow pulls in cell A1 and B1 and multiply them.
I do not need to export back into excel I just want to make python do all the work and use excel purely as reference material.
Thank you in advance for all your advice and input!!
Try pandas as well...might be easier to work with than lists in this case
DATA :
Width Height
4 2
4 4
1 1
4 5
Code
import pandas as pd
#read the file
beam = pd.read_csv('cross_section.csv')
beam['BeamSize'] = beam['Width']*beam['Height'] #do your calculations
Output:
>>> beam
Width Height BeamSize
0 4 2 8
1 4 4 16
2 1 1 1
3 4 5 20
4 2 2 4
You can slice and dice the data as you wish.
For eg, lets say you want the fifth beam :
>>> beam.ix[4]
Width 2
Height 2
BeamSize 4
Name: 4, dtype: int64
Check this for more info:
http://pandas.pydata.org/pandas-docs/stable/
You can read directly from excel as well..
Thank you for you inputs. I have found the solution I was looking for by using Numpy.
data = np.loadtxt('C:\Users[User_Name]\Desktop[fname].csv', delimiter=',')
using that it took the data and created an array with the data that I needed. Now I am able to use the data like any other matrix or array.
If you don't mind adding a (somewhat heavy) dependency to your project, pandas has a read_excel function that can read a sheet from an Excel workbook into a pandas DataFrame object, which acts sort of like a dictionary. Then reading a cell would just amount to something like:
data = pd.read_excel('/path/to/file.xls')
cell_a1 = data.loc[1, 'a'] # or however it organizes it on import
For future readers of this question, it should be mentioned that xlrd is the "most exact" solution to your requirements. It will allow you to read data directly from an Excel file (no need to convert to CSV first).
Many other packages that read Excel files (including pandas) use xlrd themselves to provide that capability. They are useful, but "heavier" than xlrd (larger, more dependencies, may require compilation of C code, etc.).
(Incidentally, pandas happens to use both xlrd and NumPy.)
Related
I have multiple inventory tables like so:
line no
-1 qty
-2 qty
1
-
3
2
42.1 FT
-
3
5
-
4
-
10 FT
5
2
1
6
6.7
-
or
line no
qty
1
2
2
4.5 KG
3
5
4
5
13
6
AR
I want to create logic check for the quantity column using python. (The table may have more than one qty column and I need to be able to check all of them. In both examples, I have the tables formatted as dataframes.)
Acceptable criteria:
integer with or without "EA" (meaning each)
"AR" (as required)
integer or float with unit of measure
if multiple QTY columns, then "-" is also accepted (first table)
I want to return a list per page, containing the line no. corresponding to rows where quantity value is missing (line 4, second table) or does not meet acceptance criteria (line 6, table 1). If the line passes the checks, then return True.
I have tried:
qty_col = [col for col in df.columns if 'qty' in col]
df['corr_qty'] = np.where(qty_col.isnull(), False, df['line_no'])
but this creates the quantity columns as a list and yields the following
AttributeError: 'list' object has no attribute 'isnull'
Intro and Suggestions:
Welcome to StackOverflow. Some general tips when asking questions on S.O. include as much information as possible. In addition, always identify the libraries you want to use and the accepted approach since there can be multiple solutions to the same problem, looks like you've done that.
Also, it is best to always share all, if not, most of your attempted solutions so others can understand the thought process and fully understand the best approach to provide a potential solution.
The Solution:
It wasn't clear if the solution you are looking for required that you read the PDF to create the dataframe or if converting the PDF to a CSV and processing the data using the CSV was sufficient. I took the latter approach.
import tabula as tb
import pandas as pd
#PDF file path
input_file_path = "/home/hackernumber7/Projects/python/resources/Pandas_Sample_Data.pdf"
#CSV file path
output_file_path = "/home/hackernumber7/Projects/python/resources/Pandas_Sample_Data.csv"
#Read the PDF
#id = tb.read_pdf(input_file_path, pages='all')
#Convert the PDF to CSV
cv = tb.convert_into(input_file_path, output_file_path, "csv", pages="all")
#Read initial data
id = pd.read_csv(output_file_path, delimiter=",")
#Print the initial data
print(id)
#Create the dataframe
df = pd.DataFrame(id, columns = ['qty'])
#Print the data as a DataFrame object; boolean values when conditions met
print(df.notna())
Issue:
Pandas appears to be swapping the column data on the data frame when it is saving to CSV? What is going on
# Code
myDF.to_csv('./myDF.csv')
print(myDF)
# Print Output
dd-3 dd-4
5346177884_triplet+ 3 3
5346177884_dublet- 5 5
5346177884_dublet+ 3 3
...
1434120345_triplet+ NaN 1
1434120345_singlet+ NaN 3
# CSV File
,dd-3,dd-4
5346177884_triplet+,3.0,3
5346177884_dublet-,5.0,5
5346177884_dublet+,3.0,3
...
1434120345_triplet+,,1
1434120345_singlet+,,3
Anyone seen anything like this before?
Be sure to check the raw CSV file to make sure that it is not the tool you are using to display the CSV that is interpreting the file incorrectly. For instance pandas will output nans as blank space in a csv file. While libercalc on import can be set to merge repeat delimiters for things like space separated files with multiple spaces. If you accidentally leave that feature on when importing a csv with blanks between delimiters you may see an effect similar to what you have reported.
Issue:
# CSV Format
,h1,h2,3
obj,v1,v2,v3
# PD handling NAN for v1 & v2
,h1,h2,3
obj,,,v3
# Merge delimiter interpretation
,h1,h2,h3
obj,v3
# Resulting View
h1 h2 h3
obj_number v3
I've been trying to sort my spreadsheet by 4 columns. Using win32com, I have managed to sort by 3 columns using the below code:
excel = win32com.client.Dispatch("Excel.Application")
wb= excel.Workbooks.Open('.xlsx')
ws= wb.worksheets[0]
ws.Range(D6:D110).Sort(Key1=ws.Range('D1'), Order1=1, Key2=ws.Range('E1'), Order2=2, Key3=ws.Range('G1'), Order3=3, Orientation=1)
However, when I try to add Key4, it says Key4 is an unexpected keyword argument. Is the Range.Sort function limited to only 3 keys? Is there a way to add a 4th?
Is there maybe another way to do this using pandas or openpyxl?
Thanks in advance!
Try reading in the excel sheet then sorting by header names. This assumes that your excel sheet is formatted correctly like a CSV.
import pandas as pd
df = pd.read_excel('filename.xlsx')
df = df.sort_values(['key1','key2','key3','key4'], axis=1)
df.to_excel('filename2.xlsx')
Simply sort twice or however many times needed in series of three keys.
xlAscending = 1
xlSortColumns = 1
xlYes = 1
ws.Range(D6:D110).Sort(Key1=ws.Range('D1'), Order1=xlAscending,
Key2=ws.Range('E1'), Order2=xlAscending,
Key3=ws.Range('G1'), Order3=xlAscending,
header=xlYes, Orientation=xlSortColumns)
# FOURTH SORT KEY (ADJUST TO NEEDED COLUMN)
ws.Range(D6:D110).Sort(Key1=ws.Range('H1'), Order1=xlAscending,
header=xlYes, Orientation=xlSortColumns)
By the way your Order numbers should only be 1, 2, or -4135 per the xlSortOrder constants.
I'm building a program that collects data and adds it to an ongoing excel sheet weekly (read_excel() and concat() with the new data). The issue I'm having is that I need the columns to have the same name for presentation (it doesn't look great with x.1, x.2, ...).
I only need this on the final output. Is there any way to accomplish this? Would it be too time consuming to modify pandas?
you can create a list of custom headers that will be read into excel
newColNames = ['x','x','x'.....]
df.to_excel(path,header=newColNames)
You can add spaces to the end of the column name. It will appear the same in a Excel, but pandas can distinguish the difference.
import pandas as pd
df = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]], columns=['x','x ','x '])
df
x x x
0 1 2 3
1 4 5 6
2 7 8 9
I am new to python and have looked at a number of similar problems on SO, but cannot find anything quite like the problem that I have and am therefore putting it forward:
I have an .xlsx dataset with data spread across eight worksheets and I want to do the following:
sum the values in the 14th column in each worksheet (the format, layout and type of data (scores) is the same in column 14 across all worksheets)
create a new worksheet with all summed values from column 14 in each worksheet
sort the totaled scores from highest to lowest
plot the summed values in a bar chart to compare
I cannot even begin this process because I am struggling at the first point. I am using pandas and am having trouble reading the data from one specific worksheet - I only seem to be able to read the data from the first worksheet only (I print the outcome to see what my system is reading in).
My first attempt produces an `Empty DataFrame':
import pandas as pd
y7data = pd.read_excel('Documents\\y7_20161128.xlsx', sheetname='7X', header=0,index_col=0,parse_cols="Achievement Points",convert_float=True)
print y7data
I also tried this but it only exported the entire first worksheet's data as opposed to the whole document (I am trying to do this so that I can understand how to export all data). I chose to do this thinking that maybe if I exported the data to a .csv, then it might give me a clearer view of what went wrong, but I am nonethewiser:
import pandas as pd
import numpy as np
y7data = pd.read_excel('Documents\\y7_20161128.xlsx')
y7data.to_csv("results.csv")
I have tried a number of different things to try and specify which column within each worksheet, but cannot get this to work; it only seems to produce the results for the first worksheet.
How can I, firstly, read the data from column 14 in every worksheet, and then carry out the rest of the steps?
Any guidance would be much appreciated.
UPDATE (for those using Enthought Canopy and struggling with openpyxl):
I am using Enthought Canopy IDE and was constantly receiving an error message around openpyxl not being installed no matter what I tried. For those of you having the same problem, save yourself lots of time and read this post. In short, register for an Enthought Canopy account (it's free), then run this code via the Canopy Command Prompt:
enpkg openpyxl 1.8.5
I think you can use this sample file:
First read all columns in each sheet to list of columns called y7data:
y7data = [pd.read_excel('y7_20161128.xlsx', sheetname=i, parse_cols=[13]) for i in range(3)]
print (y7data)
[ a
0 1
1 5
2 9, a
0 4
1 2
2 8, a
0 5
1 8
2 5]
Then concat all columns together, I add keys which are used for axis x in graph, sum all columns, remove second level of MultiIndex (a, a, a in sample data) by reset_index and last sort_values:
print (pd.concat(y7data, axis=1, keys=['a','b','c']))
a b c
a a a
0 1 4 5
1 5 2 8
2 9 8 5
summed = pd.concat(y7data, axis=1, keys=['a','b','c'])
.sum()
.reset_index(drop=True, level=1)
.sort_values(ascending=False)
print (summed)
c 18
a 15
b 14
dtype: int64
Create new DataFrame df, set column names and write to_excel:
df = summed.reset_index()#.
df.columns = ['a','summed']
print (df)
a summed
0 c 18
1 a 15
2 b 14
If need add new sheet use this solution:
from openpyxl import load_workbook
book = load_workbook('y7_20161128.xlsx')
writer = pd.ExcelWriter('y7_20161128.xlsx', engine='openpyxl')
writer.book = book
writer.sheets = dict((ws.title, ws) for ws in book.worksheets)
df.to_excel(writer, "Main", index=False)
writer.save()
Last Series.plot.bar:
import matplotlib.pyplot as plt
summed.plot.bar()
plt.show()
From what I understand, your immediate problem is managing to load the 14th column from each of your worksheets.
You could be using ExcelFile.parse instead of read_excel and loop over your sheets.
xls_file = pd.ExcelFile('Documents\\y7_20161128.xlsx')
worksheets = ['Sheet1', 'Sheet2', 'Sheet3']
series = [xls_file.parse(sheet, parse_cols=[13]) for sheet in worksheets]
df = pd.DataFrame(series)
And from that, sum() your columns and keep going.
Using ExcelFile and then ExcelFile.parse() has the advantage to load your Excel file only once, and iterate over each worksheet. Using read_excel makes your Excel file to be loaded in each iteration, which is useless.
Documentation for pandas.ExcelFile.parse.