I made an array called list_of_rows, and looped appending list_of_cells to aforemention list_of_rows
I then attempt to make a csv file by creating one and assigning a variable called writer and well, it gives me a blank file
outfile = open("./inmates.csv", "wb")
writer = csv.writer(outfile)
writer.writerows(list_of_rows)
Its supposed to give me a list of rows with cells and I don't know what's wrong.
Related
Here is my current code below.
I have a specific range of cells (from a specific sheet) that I am pulling out of multiple (~30) excel files. I am trying to pull this information out of all these files to compile into a single new file appending to that file each time. I'm going to manually clean up the destination file for the time being as I will improve this script going forward.
What I currently have works fine for a single sheet but I overwrite my destination every time I add a new file to the read in list.
I've tried adding the mode = 'a' and a couple different ways to concat at the end of my function.
import pandas as pd
def excel_loader(fname, sheet_name, new_file):
xls = pd.ExcelFile(fname)
df1 = pd.read_excel(xls, sheet_name, nrows = 20)
print(df1[1:15])
writer = pd.ExcelWriter(new_file)
df1.insert(51, 'Original File', fname)
df1.to_excel(new_file)
names = ['sheet1.xlsx', 'sheet2.xlsx']
destination = 'destination.xlsx'
for name in names:
excel_loader(name, 'specific_sheet_name', destination)
Thanks for any help in advance can't seem to find an answer to this exact situation on here. Cheers.
Ideally you want to loop through the files and read the data into a list, then concatenate the individual dataframes, then write the new dataframe. This assumes the data being pulled is the same size/shape and the sheet name is the same. If sheet name is changing, look into zip() function to send filename/sheetname tuple.
This should get you started:
names = ['sheet1.xlsx', 'sheet2.xlsx']
destination = 'destination.xlsx'
#read all files first
df_hold_list = []
for name in names:
xls = pd.ExcelFile(name)
df = pd.read_excel(xls, sheet_name, nrows = 20)
df_hold_list.append(df)
#concatenate dfs
df1 = pd.concat(df_hold_list, axis=1) # axis is 1 or 0 depending on how you want to cancatenate (horizontal vs vertical)
#write new file - may have to correct this piece - not sure what functions these are
writer = pd.ExcelWriter(destination)
df1.to_excel(destination)
I am trying to read a CSV file that has four parts that are on the same page but distinguished by putting some empty rows in the middle of the spreadsheet. I want to somehow ask pandas to stop reading the rest of the file as soon as it finds the empty row.
Edit: I need to elaborate on the problem. I have a CSV file, that has 4 different sections that separated with 3-4 empty rows. I need to extract each of these sections or at least the first section. In other words, I want read_csv stop when it finds the first empty row(of course after skipping rows with detail about the file)
url = urlopen("https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/30_Industry_Portfolios_CSV.zip")
zipfile = ZipFile(BytesIO(url.read()))
data = pd.read_csv(zipfile.open('30_Industry_Portfolios.CSV'),
header = 0, index_col=0,
skiprows=11,parse_dates=True)
You could use a generator.
Suppose the csv module is generating rows.
(We might use yield from sheet,
except that we'll change the loop in a moment.)
import csv
def get_rows(csv_fspec, skip_rows=12):
with open(csv_fspec) as fin:
sheet = csv.reader(fin)
for _ in range(skip_rows):
next(sheet) # discard initial rows
for row in sheet:
yield row
df = pd.DataFrame(get_rows(my_csv))
Now you want to ignore rows after encountering some condition,
perhaps after initial column is empty.
Ok, that's simple enough, just change the loop body:
for row in sheet:
if row[0]:
yield row
else:
break # Ignore rest of input file.
I have this script that reads a CSV and saves the second column to a list, I'm trying to get it to write the contents of the list to a new CSV. The problem is every entry should have its own row but the new file sets everything into the same row.
I've tried moving the second with open code to within the first with open and I've tried adding a for loop to the second with open but no matter what I try I don't get the right results.
Here is the code:
import csv
col_store=[]
with open('test-data.csv', 'r') as rf:
reader = csv.reader(rf)
for row in reader:
col_store.append(row[1])
with open('meow.csv', 'wt') as f:
csv_writer = csv.writer(f)
csv_writer.writerows([col_store])
In your case if you have a column of single letters/numbers then Y.R answer will work.
To have a code that works in all cases, use this.
with open('meow.csv', 'wt') as f:
csv_writer = csv.writer(f)
csv_writer.writerows(([_] for _ in col_store))
From here it is mentioned that writerows expect an an iterable of row objects. Every row object should be an iterable of strings or numbers for Writer objects
The problem is that you are using 'writerows' treating 'col_store' as a list with one item.
The simplest approach to fixing this is calling
csv_writer.writerows(col_store)
# instead of
csv_writer.writerows([col_store])
However, this will lead to a probably unwanted result - having blank lines between the lines.
To solve this, use:
with open('meow.csv', 'wt', newline='') as f:
csv_writer = csv.writer(f)
csv_writer.writerows(col_store)
For more about this, see CSV file written with Python has blank lines between each row
Note: writerows expects 'an iterable of row objects' and 'row objects must be an interable of strings or numbers'.
(https://docs.python.org/3/library/csv.html)
Therefore, in the generic case (trying to write integers for examlpe), you should use Sam's solution.
I want to delete a particular row from a given user input that matches with a column.
Let's say I get an employee ID and delete all it's corresponding values in the row.
Not sure how to approach this problem and other sources suggest using a temporary csv file to copy all values and re-iterate.
Since these are very primitive requirements, I would just do it manually.
Read it line by line - if you want to delete the current line, just don't write it back.
If you want to delete a column, for each line, parse it as csv (using the module csv - do not use .split(',')!) and discard the correct column.
The upside of these solutions is that it's very light on the memory and as fast as it can be runtime-wise.
That's pretty much the way to do it.
Something like:
import shutil
file_path = "test.csv"
# Creates a test file
data = ["Employee ID,Data1,Data2",
"111,Something,Something",
"222,Something,Something",
"333,Something,Something"]
with open(file_path, 'w') as write_file:
for item in data:
write_file.write(item + "\n")
# /Creates a test file
input("Look at the test.csv file if you like, close it, then press enter.")
employee_ID = "222"
with open(file_path) as read_file:
with open("temp_file.csv", 'w') as temp_file:
for line in read_file:
if employee_ID in line:
next(read_file)
temp_file.write(line)
shutil.move("temp_file.csv", file_path)
If you have other data that may match the employee ID, then you'll have to parse the line and check the employee ID column specifically.
I have this in csv file:
Titre,a,b,c,d,e
01,jean,paul,,
01,,,jack,
02,jeanne,jack,,
02,,,jean
and i want :
Titre,a,b,c,d,e
01,jean,paul,jack,
02,jeanne,jack,,jean
can you help me ?
In general, a good approach is to read the csv file and iterate through the rows using Python's CSV module.
CSV will create an iterator that will let you loop through your file like this:
import csv
with open('your filename.csv', 'r') as infile:
reader = csv.reader(infile)
for line in reader:
for value in line:
# Do your thing
You're going to need to construct a new data set that has different properties. The requirements you described:
Ignore any empty cells
Any time you encounter a row that has a new index number, add a new row to your new data set
Any time you encounter a row that has an index number you've seen before, add it to the row that you already created (except for that index number value itself)
I'm not writing that part of the code for you because you need to learn and grow. It's a good task for a beginner.
Once you've constructed that data set, it will look like this:
example_processed_data = [["Titre","a","b","c","d","e"],
["01","jean","paul","jack"],
["02","jeanne","jack","","jean"]]
You can then create a CSV writer, and create your outfile by iterating over that data, similarly to how you iterated over the infile:
with open('outfile.csv', 'w') as outfile:
writer = csv.writer(outfile)
for line in example_processed_data:
writer.writerow(line)
print("Done! Wrote", len(example_processed_data), "lines to outfile.csv.")