I'm "pseudo" creating a .bib file by reading a csv file and then following this structure writing down every thing including newline characters. It's a tedious process but it's a raw form on converting csv to .bib in python.
I'm using Pandas to read csv and write row by row, (and since it has special characters I'm using latin1 encoder) but I'm getting a huge problem: it only reads the first row. From the official documentation I'm using their method on reading row by row, which only gives me the first row (example 1):
row = next(df.iterrows())[1]
But if I remove the next() and [1] it gives me the content of every column concentrated in one field (example 2).
Why is this happenning? Why using the method in the docs does not iterate through all rows nicely? How would be the solution for example 1 but for all rows?
My code:
import csv
import pandas
import bibtexparser
import codecs
colnames = ['AUTORES', 'TITULO', 'OUTROS', 'DATA','NOMEREVISTA','LOCAL','VOL','NUM','PAG','PAG2','ISBN','ISSN','ISSN2','ERC','IF','DOI','CODEN','WOS','SCOPUS','URL','CODIGO BIBLIOGRAFICO','INDEXAÇÕES',
'EXTRAINFO','TESTE']
data = pandas.read_csv('test1.csv', names=colnames, delimiter =r";", encoding='latin1')#, nrows=1
df = pandas.DataFrame(data=data)
with codecs.open('test1.txt', 'w', encoding='latin1') as fh:
fh.write('#Book{Arp, ')
fh.write('\n')
rl = data.iterrows()
for i in rl:
ix = str(i)
fh.write(' Title = {')
fh.write(ix)
fh.write('}')
fh.write('\n')
PS: I'm new to python and programming, I know this code has flaws and it's not the most effective way to convert csv to bib.
The example row = next(df.iterrows())[1] intentionally only returns the first row.
df.iterrows() returns a generator over tuples describing the rows. The tuple's first entry contains the row index and the second entry is a pandas series with your data of the row.
Hence, next(df.iterrows()) returns the next entry of the generator. If next has not been called before, this is the very first tuple.
Accordingly, next(df.iterrows())[1] returns the first row (i.e. the second tuple entry) as a pandas series.
What you are looking for is probably something like this:
for row_index, row in df.iterrows():
convert_to_bib(row)
Secondly, all your writing to your file handle fh must happen within the block with codecs.open('test1.txt', 'w', encoding='latin1') as fh:
because at the end of the block the file handle will be closed.
For example:
with codecs.open('test1.txt', 'w', encoding='latin1') as fh:
# iterate through all rows
for row_index, row in df.iterrows():
# iterate through all elements in the row
for colname in df.columns:
row_element = row[colname]
fh.write('%s = {%s},\n' % (colname, str(row_element)))
Still I am not sure if the names of the columns exactly match the bibtex fields you have in mind. Probably you have to convert these first. But I hope you get the principle behind the iterations :-)
Related
Have a question. If I run this
code, I get exactly what I have in the CSV. My target is to get a text/data/csv file which would look like this:
['red', 'green', 'blue'] meaning:
Converting a single column into a row.
While converting to row, entering a comma to differentiate values.
Is it possible to do it through Python? Is there any online materials I can look into?
The csv file is read sequentially, that is, you will always get the data back one row at a time. However, you can build an array of just the values you want as you read the file and discard the rest of the data.
import csv
with open('example.csv') as csvfile:
colours = []
for row in csv.reader(csvfile, delimiter=','):
if len(row) <= 3:
continue
colours.append(row[3])
You can achieve this code using pandas. Try this code:
import pandas as pd
df = pd.read_csv("input csv file")
df = df.T
df.to_csv("output csv file")
Update if this is what you are looking for
Without using any extra libraries, you could write a function that takes a specific column, and returns that column as an array.
Such a function might look like this.
def column_to_row(col_num, csv):
return list(row[col_num] for row in csv)
Then you can extract whatever column you want, or iterate through the whole csv like this.
new_csv = []
for i in range(0, len(csv[0]):
new_csv = new_csv.append(column_to_row(i, csv))
I want to create a program in which reads a CSV file and writes in another file. My problem is, the file I'm ready is kinda big and I don't want to go through every column by doing this:
columns = defaultdict(list)
reader = csv.DictReader(csvfile)
for row in reader:
for (k,v) in row.items():
columns[k].append(v)
print(columns['name'])
print(columns['id'])
...
I wanted to, instead, do columns[0] to find 'name', and so on. Is there any way I can do this?
You are now reading the CSV with a DictReader this creates the columns based on names, in your case you could just use the reader:
columns = defaultdict(list)
reader = csv.reader(csvfile)
next(reader) # to skip the header row
for row in reader:
for i, v in enumerate(row):
columns[i].append(v)
print(columns[0])
print(columns[1])
I'm not sure that I understand your question. If you are asking, "can I read only the first column?", then the short answer is no. CSV is specifically designed to read a fixed number of columns from variable length records. More specifically, the data is organized as a list of rows, not a list of columns. You can't just seek past what you don't want to read. It sounds like what you are trying to do is reorganized your data into columns.
If you want to minimize the processing of what you do read, it sounds like all you need to do is use csv.reader and skip the first row containing the header. Each row from the reader will return a list of strings and the construction of this list should be less expensive than a map.
If you collect the list of rows you can then put it in a numpy array. A numpy array will allow you to access columns (e.g., x[:, 0]) or rows (e.g., x[0, :]).
Given that I am not entirely sure what you are asking, my answers may not not be what you are looking for; however, whatever your problem is, I am certain you cannot avoid reading the entire file.
I'm doing some manipulation on a CSV file using Python and the csv module. I take a CSV file, do some operations, and output an XML file. Very simplified, but the input data looks similar to this:
name,group
joe,staff
jane,student
bill,staff
barry,support
jack,student
I have a list as follows:
outputList = ['staff', 'support']
Essentially, what I want to do is remove the line of data if the group field isn't contained in the outputList. So what I would end up with is:
name,group
joe,staff
bill,staff
barry,support
The main reason I need to remove the rows is because I then need to sort by outputList (which is a lot longer than in this example, and in a specific non-alphabetical order).
Doing the sorting is relatively easy:
csvData = sorted(csvData, key=lambda k: (outputList.index(k['group'])))
However, obviously without removing the rows that aren't needed I get an error that the group value isn't in the outputList.
Is there an easy way of removing the data, or do I just need to iterate over each row and check whether the value is present? I've seen methods of doing it when you just have two lists. E.G.
data = ['staff', 'support', 'student']
csvData = [data for data in csvData if data not in outputList]
There's no other way to filter the data without scanning all the data of course, you can simply do something like this:
import csv
def parser(fp, groups):
with open(fp) as fin:
reader = csv.reader(fp)
for row in reader:
if row[1] in groups:
yield row
csvData = parser('~/some_loc/file.csv', outputList)
Load your csv into a pandas dataframe df.
The you can use:
df = df[df.group.isin(outputList)]
Isin creates a boolean series(mask) which you can use to select only the relevant rows.
I have a very large csv file with millions of rows and a list of the row numbers that I need.like
rownumberList = [1,2,5,6,8,9,20,22]
I know there is something called skiprows that helps to skip several rows when reading csv file like that
df = pd.read_csv('myfile.csv',skiprows = skiplist)
#skiplist would contain the total row list deducts rownumberList
However, since the csv file is very large, directly selecting the rows that I need could be more efficient. So I was wondering are there any methods to select rows when using read_csv? Not try to select rows using dataframe afterwards, since I try to minimize the time of reading file.Thanks.
There is a parameter called nrows : int, default None
Number of rows of file to read. Useful for reading pieces of large files (Docs)
pd.read_csv(file_name,nrows=int)
In case you need some part in the middle. Use both skiprows as well as nrows in read_csv.if skiprows indicate the beginning rows and nrows will indicate the next number of rows after skipping eg.
Example:
pd.read_csv('../input/sample_submission.csv',skiprows=5,nrows=10)
This will select data from the 6th row to 16 row
Edit based on comment:
Since there is a list this one might help i.e
li = [1,2,3,5,9]
r = [i for i in range(max(li)) if i not in li]
df = pd.read_csv('../input/sample_submission.csv',skiprows=r,nrows= max(li))
# This will skip the rows you dont want as well as limit the number of rows to maximum of the list.
import pandas as pd
rownumberList = [1,2,5,6,8,9,20,22]
df = pd.read_csv('myfile.csv',skiprows=lambda x: x not in rownumberList)
for pandas 0.25.1, pandas read_csv, you can pass callable function to skiprows
I am not sure about read_csv() from Pandas (there is though a way to use an iterator for reading a large file in chunks), but you can read the file line by line (lazy-loading, not reading the whole file in memory) with csv.reader (or csv.DictReader), leaving only the desired rows with the help of enumerate():
import csv
import pandas as pd
DESIRED_ROWS = {1, 17, 28}
with open("input.csv") as input_file:
reader = csv.reader(input_file)
desired_rows = [row for row_number, row in enumerate(reader)
if row_number in DESIRED_ROWS]
df = pd.DataFrame(desired_rows)
(assuming you would like to pick random/discontinuous rows and not a "continuous chunk" from somewhere in the middle - in that case #James's idea to have "start and "stop" would work generally better).
import pandas as pd
df = pd.read_csv('Data.csv')
df.iloc[3:6]
Returns rows 3 through 5 and all columns.
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iloc.html
From de documentation you can see that skiprows can take an integer or a list as values to remove some lines.
So basicaly you can tell it to remove all but those you want. For this you first need to know the number in lines in the file (best if you know beforehand) by open it and counting as following:
with open('myfile.csv') as f:
row_count = sum(1 for row in f)
Now you need to create the complementary list (here are sets but also works, don't know why). First you create the one from 1 to the number of rows and then substract the numbers of the rows you want to read.
skiplist = set(range(1, row_count+1)) - set(rownumberList)
Finally you can read the csv as normal.
df = pd.read_csv('myfile.csv',skiprows = skiplist)
here is the full code:
import pandas as pd
with open('myfile.csv') as f:
row_count = sum(1 for row in f)
rownumberList = [1,2,5,6,8,9,20,22]
skiplist = set(range(1, row_count+1)) - set(rownumberList)
df = pd.read_csv('myfile.csv', skiprows=skiplist)
you could try this
import pandas as pd
#making data frame from a csv file
data = pd.read_csv("your_csv_flie.csv", index_col ="What_you_want")
# retrieving multiple rows by iloc method
rows = data.iloc [[1,2,5,6,8,9,20,22]]
You will not be able to circumvent the read time when accessing a large file. If you have a very large CSV file, any program will need to read through it at least up to the point where you want to begin extracting rows. Really, that is what databases are designed for.
However, if you want to extract rows 300,000 to 300,123 from a 10,000,000 row CSV file, you are better off reading just the data you need into Python before converting it to a data frame in Pandas. For this you can use the csv module.
import csv
import pandas
start = 300000
stop = start + 123
data = []
with open('/very/large.csv', 'r') as fp:
reader = csv.reader(fp)
for i, line in enumerate(reader):
if i >= start:
data.append(line)
if i > stop:
break
df = pd.DataFrame(data)
for i in range (1,20)
the first parameter is the first row and the last parameter is the last row...
Started learning python after lots of ruby experience. With that context in mind:
I have a csv file that looks something like this:
city_names.csv
"abidjan","addis_ababa","adelaide","ahmedabad"
With the following python script I'd like to read this into a list:
city_names_reader.py
import csv
city_name_file = r"./city_names.csv"
with open(city_name_file, 'rb') as file:
reader = csv.reader(file)
city_name_list = list(reader)
print city_name_list
The result surprised me:
[['abidjan', 'addis_ababa', 'adelaide', 'ahmedabad']]
Any idea why I'm getting a nested list rather than a 4-element list? I must be overlooking something self-evident.
A CSV file represents a table of data. A table contains both columns and rows, like a spreadsheet. Each line in a CSV file is one row in the table. One row contains multiple columns, separated by ,
When you read a CSV file you get a list of rows. Each row is a list of columns.
If your file have only one row you can easily just read that row from the list:
city_name_list = city_name_list[0]
Usually each column represent some kind of data (think "column of email addresses"). Each row then represent a different object (think "one object per row, each row can have one email address"). You add more objects to the table by adding more rows.
It is not common with wide tables. Wide tables are those that grow by adding more columns instead of rows. In your case you have only one kind of data: city names. So you should have one column ("name"), with one row per city. To get city names from your file you could then read the first element from each row:
city_name_list = [row[0] for row in city_name_list]
In both cases you can flatten the list by using itertools.chain:
city_name_list = itertools.chain(city_name_list)
As others suggest, your file is not an idiomatic CSV file. You can simply do:
with open(city_name_file, "rb") as fp:
city_names_list = fp.read().split(",")
Based on comments, here is a possible solution:
import csv
city_name_file = r"./city_names.csv"
city_name_list = []
with open(city_name_file, 'rb') as file:
reader = csv.reader(file)
for item in reader:
city_name_list += item
print city_name_list