I have a file daiy.csv which is updated on daily basis. The script fetches values out of it, checks against some values and then prints out some info based on those checks. My problem is that almost once a week, the column numbers in daily.csv either increase or decrease. Then I have to find the new column number for my desired values. I was wondering if there is a way I can use header values in my if checks so regardless the column numbers change, I can still run my script.
with open('daily.csv','rb')as f:
reader=csv.reader(f)
#next(reader, None) #Skipping the header
for row in reader:
#if row[3]=='M2' and (float(row[38]) > 60):
try:
if (row[3]=='M2' or row[3]=='M4' or row[3]=='M3') and (float(row[37]) > 60):
print row[1] + "/" + row[2] + "/" + row[3] + " : " + row[37]
if (row[3]=='M2' or row[3]=='M4' or row[3]=='M3') and (float(row[37]) < 70):
print row[1] + "/" + row[2] + "/" + row[3] + " : " + row[37]
except:
pass
This seems like a problem that pandas (http://pandas.pydata.org/) can easily help you solve. Assuming that all of your columns have unique headers you can just extract the relevant columns you care about. Let's say that your column names are 'A', 'B', and 'C' and you don't want to analyze column 'B'.
import pandas as pd
df = pd.read_csv('daily.csv')
columns_to_analyze = ['A', 'C']
df = df[columns_to_analyze]
Without knowing more specifics of how 'daily.csv' is formatted that's as much as I can confidently say...
Related
I have a cycle in which on every iteration I export the pandas dataframe to a CSV file. The problem is that i got an output as you see in the first picture, but i need to get something similar to the second one.
I also tried with some encoding type, such as utf-8, utf-16, but nothing changed.
The only difference between my solution and the ones found online is that my dataframe is built from a pickle file, but I don't think this is the problem.
for pickle_file in files:
key = pickle_file.split('/')[5].split('\\')[1] + '_' + pickle_file.split('/')[5].split('\\')[4]
with lz4.frame.open(pickle_file, "rb") as f:
while True:
try:
diz[key].append(pickle.load(f))
except EOFError:
break
for key in diz.keys():
a = diz[key]
for j in range(len(a)):
t = a[j]
for index,row in t.iterrows():
if row['MODE'] != 'biflow':
w = row['W']
feature = row['FEATURE']
mean = row['G-MEAN']
rmse = row['RMSE']
df.loc[-1] = [w] + [feature] + [rmse] + [mean] + [key]
df.index = df.index + 1
df = df.sort_values(by = ['W'])
df.to_csv(path + key + '.csv', index = False)
df = df[0:0]
The data is correctly formed. What you need to do is split each row into columns. In MS Excel it's Data > Text to Columns and then follow the function wizard.
If you are using a different application for opening the data, just google how to split text row data into columns for that application.
I am new to python and need some help understanding why my code continues to run past what I originally believed are the boundaries that I have set for rows and columns:
from openpyxl import load_workbook
wb = load_workbook("x_test.xlsx")
ws = wb.active
emptycellcount = 0
maxrow=50
maxcol=20
for col in ws.iter_cols(min_row = 0, max_row = maxrow):
for row in ws.iter_rows(min_col = 0, max_col = maxcol):
emptycellcount=0
for cell in row:
if cell.value == None:
emptycellcount += 1
print("The presence of an empty row is at " + str(cell.row) + " " + str(cell.column) + " " + str(emptycellcount))
elif cell.value != None:
emptycellcount = 0
emptyrow = cell.row
print("not empty " + str(cell.row) + " " + str(cell.column) + " " + str(emptycellcount))
break
Right now this code runs through the entire used range of my worksheet "x_text.xlsx" but I would like it to do 3 things:
only iterate through a maximum of 20 cells (columns) across 50 rows
stop running the code when an entirely empty row is found within the 50x20 cell range
save the location of the empty row to the variable "emptyrow" to be referenced again later
I have been working at this for a few days now and have yet to figure out a workable solution. Any help would be highly appreciated.
Thank you in advance!
When using apply in the below way, the values that get passed as "row" are exclusively those from the first row of the dataframe.
df.apply(make_word_file, axis=1)
Oddly, the file name created in the document.save() is correct. newname has the correct values in row['case_name']. However if I print(row) it prints the values from the first row.
def make_word_file(row):
for key, value in mapfields.items():
# print(row)
regex1 = re.compile(key)
replace1 = str(row[value])
docx_replace_regex(document, regex1 , replace1)
newname = remove(row['case_name'], '\/:*?"<>|,.')
print(newname)
document.save(datadir + row["datename"] + "_" + row["court"] + "_" + newname + ".docx")
I expected print(row) to print the values from each row in the dataframe not just the 1st.
EDIT for clarity:
This script is a mail merge which makes .docx word files.
mapfields is a dict in the format of regex:column name. document is a docx-python object.
mapfields = {
"VARfname": "First Name",
"VARlname": "Last Name",
}
This ended up being a loop/python-docx issue not a pandas one.
The document object was being overwritten, leaving nothing for the regex to find after the first one. Loading the document template in the function fixed the issue.
def make_word_file(case_row):
document_template = Document(directory + fname)
document = document_template
for key, value in mapfields.items():
regex1 = re.compile(key)
replace1 = str(case_row[value])
docx_replace_regex(document, regex1 , replace1)
document.save(location + ".docx")
So I was making a quick script to loop through a bunch of sheets in an excel file (22 to be exact) and what I wanted to do was the following:
Open the excel sheet and open the sheet named "All" which contained a list of names and then loop through each name and do the following
To loop through all the other 22 sheets in the same workbook and look through each one for the name, which I knew was in the 'B' column.
If the name were to be found, I wanted to take all the columns in that row containing the data for that name and these columns were from A-H
Then copy and paste them next to the original name (same row) in the 'All sheet' while leaving a bit of a space between the original name and the others.
I wanted to do this for all 22 sheets and for the 200+ names listed in the 'All' sheet, my code is as follows:
import openpyxl, pprint
columns = ['A','B','C','D','E','F','G','H']
k = 10
x = 0
def colnum_string(n):
string = ""
while n > 0:
n, remainder = divmod(n - 1, 26)
string = chr(65 + remainder) + string
return string
print("Opening Workbook...")
wb = openpyxl.load_workbook('FileName.xlsx')
sheet_complete = wb.get_sheet_by_name("All")
row_count_all = sheet_complete.max_row
for row in range(4, row_count_all+1):
k = 10
cell = 'B' + str(row)
print(cell)
name = sheet_complete[cell].value
for i in range(2, 23):
sheet = wb.get_sheet_by_name(str(1995 + i))
row_count = sheet.max_row
for row2 in range(2, row_count+1):
cell2 = 'B' + str(row2)
name2 = sheet[cell].value
if name == name2:
x = x + 1
for z in range(0,len(columns)):
k = k + 1
cell_data = sheet[columns[z] + str(row2)].value
cell_target = colnum_string(k) + str(row)
sheet_complete[cell_target] = cell_data
wb.save('Scimago Country Ranking.xlsx')
print("Completed " + str(x) + " Task(s)")
break
The problem is that it keeps looping with the first name only, so it goes through all the names but when it comes to copying and pasting the data, it just redoes the first name so in the end, I end up with all the names in the 'All' sheet and next to each one is the data for the first name repeated over and over. I can't see what's wrong with my code but forgive me if it's a silly mistake as I'm kind of a beginner in these excel parsing scripts. print statements were for testing reasons.
P.S I know I'm using a deprecated function and I will change that, I was just too lazy to do it since it seems to still work fine and if that's the problem then please let me know.
I'm trying to copy an entire column over using OpenPyXL. Google seems to offer a lot of examples using ranges, but not for an entire column.
I have a workbook with a single worksheet with a load of dates in column A and column JX (A contains monthly dates, JX contains quarterly dates). I want the monthly dates column (in A:A) to be copied over to each worksheet ending in 'M' in my target workbook, and the quarterly dates column (in JX:JX) to the worksheets ending in 'Q'.
However, for some reason the last nested for loop, for src, dst in zip(ws_base[monthRange], ws_target['A:A']): is only copying the first cell, and nothing else. It looks like I'm identifying the correct column with my monthRange and quarterRange strings, but Python isn't looping through the whole column despite the fact that I've got two ranges defined.
Does anyone have any ideas?
# Load the target workbook
targetwb = openpyxl.load_workbook('pythonOutput.xlsx')
# Load the source workbook
wb_base = openpyxl.load_workbook('Baseline_IFRS9_' + reportingMonth+'.xlsx')
# Go to row 9 and find "Geography:" to identify the relevant
# month and quarter date columns
sentinel = u"Geography:"
ws_base = wb_base.active
found = 0
dateColumns = []
for column in ws_base:
for cell in column:
if cell.value == sentinel:
dateColumns.append(cell.column) #
found + 1
if found == 2:
break
ColumnM = dateColumns[0]
ColumnQ = dateColumns[1]
print('Monthly col is ' + ColumnM)
print('Quarterly col is ' + ColumnQ)
IndexM = int(openpyxl.utils.column_index_from_string(str(ColumnM)))
IndexQ = int(openpyxl.utils.column_index_from_string(str(ColumnQ)))
print('Monthly col index is ' + str(IndexM))
print('Quarterly col index is ' + str(IndexQ))
print('Proceeding to paste into our new workbook...')
sheetLoop = targetwb.get_sheet_names()
for sheets in sheetLoop:
if sheets.endswith('Q'):
ws_target = targetwb[sheets]
quarterRange = ColumnQ + ':' + ColumnQ
print('Copying and pasting quarterly dates into: ' + sheets)
for src, dst in zip(ws_base[quarterRange], ws_target['A:A']):
dst.value = src.value
elif sheets.endswith('M'):
ws_target = targetwb[sheets]
monthRange = ColumnM + ':' + ColumnM
print('Copying and pasting monthly dates into: ' + sheets)
for src, dst in zip(ws_base[monthRange], ws_target['A:A']):
dst.value = src.value
targetwb.save('pythonOutput.xlsx')
Here's a simpler form of my problem.
import openpyxl
wb1 = openpyxl.load_workbook('pythonInput.xlsx')
ws1 = wb1.active
wb2 = openpyxl.load_workbook('pythonOutput.xlsx')
ws2 = wb2.active
for src, dst in zip(ws1['A:A'], ws2['B:B']):
print( 'Printing from ' + str(src.column) + str(src.row) + ' to ' + str(dst.column) + str(dst.row))
dst.value = src.value
wb2.save('test.xlsx')
So the problem here is that the for loop only prints from A1 to B1. Shouldn't it be looping down across rows..?
When you load a new XLSX in a spreadsheet editor, you see lots and lots of empty cells in a grid. However, these empty cells are actually omitted from the file, and they will be only written once they have a non-empty value. You can see for yourself: XLSX is essentially a bunch of ZIP-compressed XMLs, which can be opened with any archive manager.
In a similar fashion, new cells in OpenPyXL are only created when you access them. The ws2['B:B'] range only contains one cell, B1, and zip stops when the shortest iterator is exhausted.
With this in mind, you can iterate through the source range and use explicit coordinates to save the values in correct cells:
import openpyxl
wb1 = openpyxl.load_workbook('pythonInput.xlsx')
ws1 = wb1.active
wb2 = openpyxl.load_workbook('pythonOutput.xlsx')
ws2 = wb2.active
for cell in ws1['A:A']:
print('Printing from ' + str(cell.column) + str(cell.row))
ws2.cell(row=cell.row, column=2, value=cell.value)
wb2.save('test.xlsx')