The problem that I have to solve:
I'm trying to automate several processes in excel. I'm currently stuck on the first one. (Also I'm pretty weak at using excel so I apologize in advance if some of the things I saw don't make sense. I scraped data from the internet and inputted into an excel file. I concat'ed that data with a spreadsheet I already had. Here's the code I used to combine files.
import numpy as np
import pandas as pd
def MergeFiles():
#find both csv files on computer
baseData = pd.read_csv('pathname') #keep this on the left
scrapedData = pd.read_csv('pathname') #keep this on the right
mergedFile = pd.concat([baseData, scrapedData], axis = 1)
mergedFile.to_csv('pathname', index = False)
MergeFiles()
What I want to do:
Col1 Col2
c 1
b 2
a 3
-Alphabetically Order Col 1 and values in col2 also shift
Col1 Col2
a 3
b 2
c 1
I'm trying to link columns together so if I try to sort all rows go through the same position shift.
Also any help would be appreciated, I tried looking into Pandas documentation and I couldn't find anything related to this problem. I probably missed something so any help would be appreciated!
So apparently the pandas library does all of this automatically through sort_values()
So
scrapedData = scrapedData.sort_values(by = ['colName'], ascending=True,) #sort the scrapedData
scrapedData.to_csv('pathName', index = False) #replace the file
would do the trick
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())
I have a dataset which contains 6 columns TIME1 to TIME6, amongst others. For each of these I need to apply the code below (which is shown for 2 columns). LISTED is a prepared list of the possible elements to be seen in these columns.
Is there a way to do this without writing the same 2 lines 6 times?
df['PART1'] = df['TIME1'].astype('category')
df['PART1'].cat.set_categories(LISTED, inplace=True)
df['PART2'] = df['TIME2'].astype('category')
df['PART2'].cat.set_categories(LISTED, inplace=True)
For astype(first line of code), I tried the following:
for col in ['TIME1', 'TIME2', 'TIME3', 'TIME4', 'TIME5', 'TIME6']:
df_col = df[col].astype('category')
I think this works (not sure how to check without the whole code working). But how could I do something similar for the second line of code with the set_categories etc?
In short, I'm looking for something short/more elegant that just copying and modifying the same 2 lines 6 times.
I am new to python, any help is greatly appreciated.
Using python 2.7 and pandas 0.24.2
Yes it is possible! We can change the dtype of multiple columns to categorical is one go by creating CategoricalDtype
i = pd.RangeIndex(1, 7).astype(str)
df['PART' + i] = df['TIME' + i].astype(pd.CategoricalDtype(LISTED))
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.
Currenly, I am new in python scripting I am using panda, pivottablejs for creating a script. I have one csv file and I read that csv file using panda and I got the table like this.
Now, I want to generate the pivotchart using pivottablejs so for that I have to pass dataframe object in the pivot_ui();
I want to Plot in Pivot Chart the total number of issue status created for every OriginationPhase.
So I tried something like this.
LabelsReviewedByDate = issues_df.groupby(['Status','OriginationPhase'])
pivot_ui(LabelsReviewedByDate)
I know this is wrong but I am new in python scripting. So help me to find the solution.
Thank you
You can just pass the dataframe right to pivot_ui:
import pandas as pd
from pivottablejs import pivot_ui
a= [ [1,'Requirements','bug'],[2,'Design','bug'],[3,'Testing','bug'],[4,'Requirements','bug'],[5,'Requirements','Inquiry'] ]
df = pd.DataFrame(a,columns =['Issue#','OriginationPhase','Category'])
pivot_ui(df)
The pivot_table method comes to solve this problem. It works like pivot, but it aggregates the values from rows with duplicate entries for the specified columns
a= [ [1,'Requirements','bug'],[2,'Design','bug'],[3,'Testing','bug'],[4,'Requirements','bug'],[5,'Requirements','Inquiry'] ]
df = pd.DataFrame(a,columns =['Issue#','OriginationPhase','Category'])
df.pivot_table( index = 'Category',columns = 'OriginationPhase',aggfunc = lambda x: len(x) ) )
Issue#
OriginationPhase Design Requirements Testing
Category
Inquiry NaN 1 NaN
bug 1 2 1
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.