I am a beginner of Python and would like to have your opinion..
I wrote this code that reads the only column in a file on my pc and puts it in a list.
I have difficulties understanding how I could modify the same code with a file that has multiple columns and select only the column of my interest.
Can you help me?
list = []
with open(r'C:\Users\Desktop\mydoc.csv') as file:
for line in file:
item = int(line)
list.append(item)
results = []
for i in range(0,1086):
a = list[i-1]
b = list[i]
c = list[i+1]
results.append(b)
print(results)
You can use pandas.read_csv() method very simply like this:
import pandas as pd
my_data_frame = pd.read_csv('path/to/your/data')
results = my_data_frame['name_of_your_wanted_column'].values.tolist()
A useful module for the kind of work you are doing is the imaginatively named csv module.
Many csv files have a "header" at the top, this by convention is a useful way of labeling the columns of your file. Assuming you can insert a line at the top of your csv file with comma delimited fieldnames, then you could replace your program with something like:
import csv
with open(r'C:\Users\Desktop\mydoc.csv') as myfile:
csv_reader = csv.DictReader(myfile)
for row in csv_reader:
print ( row['column_name_of_interest'])
The above will print to the terminal all the values that match your specific 'column_name_of_interest' after you edit it to match your particular file.
It's normal to work with lots of columns at once, so that dictionary method of packing a whole row into a single object, addressable by column-name can be very convenient later on.
To a pure python implementation, you should use the package csv.
data.csv
Project1,folder1/file1,data
Project1,folder1/file2,data
Project1,folder1/file3,data
Project1,folder1/file4,data
Project1,folder2/file11,data
Project1,folder2/file42a,data
Project1,folder2/file42b,data
Project1,folder2/file42c,data
Project1,folder2/file42d,data
Project1,folder3/filec,data
Project1,folder3/fileb,data
Project1,folder3/filea,data
Your python program should read it by line
import csv
a = []
with open('data.csv') as csv_file:
reader = csv.reader(csv_file, delimiter=',')
for row in reader:
print(row)
# ['Project1', 'folder1/file1', 'data']
If you print the row element you will see it is a list like that
['Project1', 'folder1/file1', 'data']
If I would like to put in my list all elements in column 1, I need to put that element in my list, doing:
a.append(row[1])
Now in list a I will have a list like:
['folder1/file1', 'folder1/file2', 'folder1/file3', 'folder1/file4', 'folder2/file11', 'folder2/file42a', 'folder2/file42b', 'folder2/file42c', 'folder2/file42d', 'folder3/filec', 'folder3/fileb', 'folder3/filea']
Here is the complete code:
import csv
a = []
with open('data.csv') as csv_file:
reader = csv.reader(csv_file, delimiter=',')
for row in reader:
a.append(row[1])
Related
I have a CSV file and when I read it by importing the CSV library I get as the output:
['exam', 'id_student', 'grade']`
['maths', '573834', '7']`
['biology', '573834', '8']`
['biology', '578833', '4']
['english', '581775', '7']`
# goes on...
I need to edit it by creating a 4th column called 'Passed' with two possible values: True or False depending on whether the grade of the row is >= 7 (True) or not (False), and then count how many times each student passed an exam.
If it's not possible to edit the CSV file that way, I would need to just read the CSV file and then create a dictionary of lists with the following output:
dict = {'id_student':[573834, 578833, 581775], 'passed_count': [2,0,1]}
# goes on...
Thanks
Try using importing csv as pandas dataframe
import pandas as pd
data=pd.read_csv('data.csv')
And then use:
data['passed']=(data['grades']>=7).astype(bool)
And then save dataframe to csv as:
data.to_csv('final.csv',index=False)
It is totally possible to "edit" CSV.
Assuming you have a file students.csv with the following content:
exam,id_student,grade
maths,573834,7
biology,573834,8
biology,578833,4
english,581775,7
Iterate over input rows, augment the field list of each row with an additional item, and save it back to another CSV:
import csv
with open('students.csv', 'r', newline='') as source, open('result.csv', 'w', newline='') as result:
csvreader = csv.reader(source)
csvwriter = csv.writer(result)
# Deal with the header
header = next(csvreader)
header.append('Passed')
csvwriter.writerow(header)
# Process data rows
for row in csvreader:
row.append(str(int(row[2]) >= 7))
csvwriter.writerow(row)
Now result.csv has the content you need.
If you need to replace the original content, use os.remove() and os.rename() to do that:
import os
os.remove('students.csv')
os.rename('result.csv', 'students.csv')
As for counting, it might be an independent thing, you don't need to modify CSV for that:
import csv
from collections import defaultdict
with open('students.csv', 'r', newline='') as source:
csvreader = csv.reader(source)
next(csvreader) # Skip header
stats = defaultdict(int)
for row in csvreader:
if int(row[2]) >= 7:
stats[row[1]] += 1
print(stats)
You can include counting into the code above and have both pieces in one place. defaultdict (stats) has the same interface as dict if you need to access that.
I am asking Python to print the minimum number from a column of CSV data, but the top row is the column number, and I don't want Python to take the top row into account. How can I make sure Python ignores the first line?
This is the code so far:
import csv
with open('all16.csv', 'rb') as inf:
incsv = csv.reader(inf)
column = 1
datatype = float
data = (datatype(column) for row in incsv)
least_value = min(data)
print least_value
Could you also explain what you are doing, not just give the code? I am very very new to Python and would like to make sure I understand everything.
You could use an instance of the csv module's Sniffer class to deduce the format of a CSV file and detect whether a header row is present along with the built-in next() function to skip over the first row only when necessary:
import csv
with open('all16.csv', 'r', newline='') as file:
has_header = csv.Sniffer().has_header(file.read(1024))
file.seek(0) # Rewind.
reader = csv.reader(file)
if has_header:
next(reader) # Skip header row.
column = 1
datatype = float
data = (datatype(row[column]) for row in reader)
least_value = min(data)
print(least_value)
Since datatype and column are hardcoded in your example, it would be slightly faster to process the row like this:
data = (float(row[1]) for row in reader)
Note: the code above is for Python 3.x. For Python 2.x use the following line to open the file instead of what is shown:
with open('all16.csv', 'rb') as file:
To skip the first line just call:
next(inf)
Files in Python are iterators over lines.
Borrowed from python cookbook,
A more concise template code might look like this:
import csv
with open('stocks.csv') as f:
f_csv = csv.reader(f)
headers = next(f_csv)
for row in f_csv:
# Process row ...
In a similar use case I had to skip annoying lines before the line with my actual column names. This solution worked nicely. Read the file first, then pass the list to csv.DictReader.
with open('all16.csv') as tmp:
# Skip first line (if any)
next(tmp, None)
# {line_num: row}
data = dict(enumerate(csv.DictReader(tmp)))
You would normally use next(incsv) which advances the iterator one row, so you skip the header. The other (say you wanted to skip 30 rows) would be:
from itertools import islice
for row in islice(incsv, 30, None):
# process
use csv.DictReader instead of csv.Reader.
If the fieldnames parameter is omitted, the values in the first row of the csvfile will be used as field names. you would then be able to access field values using row["1"] etc
Python 2.x
csvreader.next()
Return the next row of the reader’s iterable object as a list, parsed
according to the current dialect.
csv_data = csv.reader(open('sample.csv'))
csv_data.next() # skip first row
for row in csv_data:
print(row) # should print second row
Python 3.x
csvreader.__next__()
Return the next row of the reader’s iterable object as a list (if the
object was returned from reader()) or a dict (if it is a DictReader
instance), parsed according to the current dialect. Usually you should
call this as next(reader).
csv_data = csv.reader(open('sample.csv'))
csv_data.__next__() # skip first row
for row in csv_data:
print(row) # should print second row
The documentation for the Python 3 CSV module provides this example:
with open('example.csv', newline='') as csvfile:
dialect = csv.Sniffer().sniff(csvfile.read(1024))
csvfile.seek(0)
reader = csv.reader(csvfile, dialect)
# ... process CSV file contents here ...
The Sniffer will try to auto-detect many things about the CSV file. You need to explicitly call its has_header() method to determine whether the file has a header line. If it does, then skip the first row when iterating the CSV rows. You can do it like this:
if sniffer.has_header():
for header_row in reader:
break
for data_row in reader:
# do something with the row
this might be a very old question but with pandas we have a very easy solution
import pandas as pd
data=pd.read_csv('all16.csv',skiprows=1)
data['column'].min()
with skiprows=1 we can skip the first row then we can find the least value using data['column'].min()
The new 'pandas' package might be more relevant than 'csv'. The code below will read a CSV file, by default interpreting the first line as the column header and find the minimum across columns.
import pandas as pd
data = pd.read_csv('all16.csv')
data.min()
Because this is related to something I was doing, I'll share here.
What if we're not sure if there's a header and you also don't feel like importing sniffer and other things?
If your task is basic, such as printing or appending to a list or array, you could just use an if statement:
# Let's say there's 4 columns
with open('file.csv') as csvfile:
csvreader = csv.reader(csvfile)
# read first line
first_line = next(csvreader)
# My headers were just text. You can use any suitable conditional here
if len(first_line) == 4:
array.append(first_line)
# Now we'll just iterate over everything else as usual:
for row in csvreader:
array.append(row)
Well, my mini wrapper library would do the job as well.
>>> import pyexcel as pe
>>> data = pe.load('all16.csv', name_columns_by_row=0)
>>> min(data.column[1])
Meanwhile, if you know what header column index one is, for example "Column 1", you can do this instead:
>>> min(data.column["Column 1"])
For me the easiest way to go is to use range.
import csv
with open('files/filename.csv') as I:
reader = csv.reader(I)
fulllist = list(reader)
# Starting with data skipping header
for item in range(1, len(fulllist)):
# Print each row using "item" as the index value
print (fulllist[item])
I would convert csvreader to list, then pop the first element
import csv
with open(fileName, 'r') as csvfile:
csvreader = csv.reader(csvfile)
data = list(csvreader) # Convert to list
data.pop(0) # Removes the first row
for row in data:
print(row)
I would use tail to get rid of the unwanted first line:
tail -n +2 $INFIL | whatever_script.py
just add [1:]
example below:
data = pd.read_csv("/Users/xyz/Desktop/xyxData/xyz.csv", sep=',', header=None)**[1:]**
that works for me in iPython
Python 3.X
Handles UTF8 BOM + HEADER
It was quite frustrating that the csv module could not easily get the header, there is also a bug with the UTF-8 BOM (first char in file).
This works for me using only the csv module:
import csv
def read_csv(self, csv_path, delimiter):
with open(csv_path, newline='', encoding='utf-8') as f:
# https://bugs.python.org/issue7185
# Remove UTF8 BOM.
txt = f.read()[1:]
# Remove header line.
header = txt.splitlines()[:1]
lines = txt.splitlines()[1:]
# Convert to list.
csv_rows = list(csv.reader(lines, delimiter=delimiter))
for row in csv_rows:
value = row[INDEX_HERE]
Simple Solution is to use csv.DictReader()
import csv
def read_csv(file): with open(file, 'r') as file:
reader = csv.DictReader(file)
for row in reader:
print(row["column_name"]) # Replace the name of column header.
I want to know the best way to reverse the lines of a big csv file (+50000 lines) in python 2.7 and rewrite it, avoiding the first line.
input:
A;B;C
1;2;3
4;5;6
output
A;B;C
4;5;6
1;2;3
I need to know how to do it in a efficient way in python 2.7.
Thank you guys,
menchopez
read the csv file using csv module and open the output also using csv module. Now you're working with lists as rows.
Use next to write the title line as-is. Now that the first line is consumed, convert the rest of the data into a list to read it fully and apply writerows on the reversed list:
import csv
with open("in.csv") as fr, open("out.csv","wb") as fw:
cr = csv.reader(fr,delimiter=";")
cw = csv.writer(fw,delimiter=";")
cw.writerow(next(cr)) # write title as-is
cw.writerows(reversed(list(cr)))
writerows is the fastest way of doing it, because it involves no python loops.
Python 3 users have to open the output file using open("out.csv","w",newline="") instead.
If you can use external libraries, the pandas library is good for large files:
import pandas as pd
# load the csv and user row 0 as headers
df = pd.read_csv("filepath.csv", header = 0)
# reverse the data
df.iloc[::-1]
If you cannot use external libraries:
import csv
with open("filepath.csv") as csvFile:
reader = csv.reader(csvFile)
# get data
data = [row for row in reader]
# get headers and remove from data
headers = data.pop(0)
# reverse the data
data_reversed = data[::-1]
# append the reversed data to the list of headers
output_data = headers.append(data_reversed)
Read as follows:
rows = []
first = True
for row in reader:
if first:
first = False
first_row = row
continue
rows.append(row)
write as follows:
rows.append(first_row)
writer.writerows(rows[::-1])
The purpose of my Python script is to compare the data present in multiple CSV files, looking for discrepancies. The data are ordered, but the ordering differs between files. The files contain about 70K lines, weighing around 15MB. Nothing fancy or hardcore here. Here's part of the code:
def getCSV(fpath):
with open(fpath,"rb") as f:
csvfile = csv.reader(f)
for row in csvfile:
allRows.append(row)
allCols = map(list, zip(*allRows))
Am I properly reading from my CSV files? I'm using csv.reader, but would I benefit from using csv.DictReader?
How can I create a list containing whole rows which have a certain value in a precise column?
Are you sure you want to be keeping all rows around? This creates a list with matching values only... fname could also come from glob.glob() or os.listdir() or whatever other data source you so choose. Just to note, you mention the 20th column, but row[20] will be the 21st column...
import csv
matching20 = []
for fname in ('file1.csv', 'file2.csv', 'file3.csv'):
with open(fname) as fin:
csvin = csv.reader(fin)
next(csvin) # <--- if you want to skip header row
for row in csvin:
if row[20] == 'value':
matching20.append(row) # or do something with it here
You only want csv.DictReader if you have a header row and want to access your columns by name.
This should work, you don't need to make another list to have access to the columns.
import csv
import sys
def getCSV(fpath):
with open(fpath) as ifile:
csvfile = csv.reader(ifile)
rows = list(csvfile)
value_20 = [x for x in rows if x[20] == 'value']
If I understand the question correctly, you want to include a row if value is in the row, but you don't know which column value is, correct?
If your rows are lists, then this should work:
testlist = [row for row in allRows if 'value' in row]
post-edit:
If, as you say, you want a list of rows where value is in a specified column (specified by an integer pos, then:
testlist = []
pos = 20
for row in allRows:
testlist.append([element if index != pos else 'value' for index, element in enumerate(row)])
(I haven't tested this, but let me now if that works).
I have 125 data files containing two columns and 21 rows of data and I'd like to import them into a single .csv file (as 125 pairs of columns and only 21 rows).
This is what my data files look like:
I am fairly new to python but I have come up with the following code:
import glob
Results = glob.glob('./*.data')
fout='c:/Results/res.csv'
fout=open ("res.csv", 'w')
for file in Results:
g = open( file, "r" )
fout.write(g.read())
g.close()
fout.close()
The problem with the above code is that all the data are copied into only two columns with 125*21 rows.
Any help is very much appreciated!
This should work:
import glob
files = [open(f) for f in glob.glob('./*.data')] #Make list of open files
fout = open("res.csv", 'w')
for row in range(21):
for f in files:
fout.write( f.readline().strip() ) # strip removes trailing newline
fout.write(',')
fout.write('\n')
fout.close()
Note that this method will probably fail if you try a large number of files, I believe the default limit in Python is 256.
You may want to try the python CSV module (http://docs.python.org/library/csv.html), which provides very useful methods for reading and writing CSV files. Since you stated that you want only 21 rows with 250 columns of data, I would suggest creating 21 python lists as your rows and then appending data to each row as you loop through your files.
something like:
import csv
rows = []
for i in range(0,21):
row = []
rows.append(row)
#not sure the structure of your input files or how they are delimited, but for each one, as you have it open and iterate through the rows, you would want to append the values in each row to the end of the corresponding list contained within the rows list.
#then, write each row to the new csv:
writer = csv.writer(open('output.csv', 'wb'), delimiter=',')
for row in rows:
writer.writerow(row)
(Sorry, I cannot add comments, yet.)
[Edited later, the following statement is wrong!!!] "The davesnitty's generating the rows loop can be replaced by rows = [[]] * 21." It is wrong because this would create the list of empty lists, but the empty lists would be a single empty list shared by all elements of the outer list.
My +1 to using the standard csv module. But the file should be always closed -- especially when you open that much of them. Also, there is a bug. The row read from the file via the -- even though you only write the result here. The solution is actually missing. Basically, the row read from the file should be appended to the sublist related to the line number. The line number should be obtained via enumerate(reader) where reader is csv.reader(fin, ...).
[added later] Try the following code, fix the paths for your puprose:
import csv
import glob
import os
datapath = './data'
resultpath = './result'
if not os.path.isdir(resultpath):
os.makedirs(resultpath)
# Initialize the empty rows. It does not check how many rows are
# in the file.
rows = []
# Read data from the files to the above matrix.
for fname in glob.glob(os.path.join(datapath, '*.data')):
with open(fname, 'rb') as f:
reader = csv.reader(f)
for n, row in enumerate(reader):
if len(rows) < n+1:
rows.append([]) # add another row
rows[n].extend(row) # append the elements from the file
# Write the data from memory to the result file.
fname = os.path.join(resultpath, 'result.csv')
with open(fname, 'wb') as f:
writer = csv.writer(f)
for row in rows:
writer.writerow(row)