I have the text file with delimiter |: file1.txt
ID|Name|Date
1|A|2017-12-19
2|B|2017-12-20
3|C|2017-12-21
And following SET: <type 'set'>
id_set = set(['1','2'])
date_set = set(['2017-12-19', '2017-12-20'])
I just want to find the matching element from set to file and write that record from file1.txt to output.txt.
Expected Output: Output.txt should get following data,
ID|Name|Date
1|A|2017-12-19
2|B|2017-12-20
You can try out this solution:
id_set = {'1','2'}
date_set = {'2017-12-19', '2017-12-20'}
# open files for reading and writing
with open('file.txt') as in_file, open('output.txt', 'w') as out_file:
# write headers
out_file.write(next(in_file))
# go over lines in file
for line in in_file:
# extract id and date
id, _, date = line.rstrip().split('|')
# keep lines have an id or date in the sets
if id in id_set or date in date_set:
out_file.write(line)
Which gives the following output.txt:
ID|Name|Date
1|A|2017-12-19
2|B|2017-12-20
If you are happy to use a 3rd party library, you can use Pandas:
import pandas as pd
from io import StringIO
mystr = StringIO("""ID|Name|Date
1|A|2017-12-19
2|B|2017-12-20
3|C|2017-12-21""")
# replace mystr with 'file1.txt'
df = pd.read_csv(mystr, sep='|')
# criteria
id_set = {'1', '2'}
date_set = {'2017-12-19', '2017-12-20'}
# apply criteria
df2 = df[df['ID'].astype(str).isin(id_set) | df['Date'].isin(date_set)]
print(df2)
# ID Name Date
# 0 1 A 2017-12-19
# 1 2 B 2017-12-20
# export to csv
df2.to_csv('file1_out.txt', sep='|')
Related
I have a poorly-structured CSV file named file.csv, and I want to split it up into multiple CSV using Python.
|A|B|C|
|Continent||1|
|Family|44950|file1|
|Species|44950|12|
|Habitat||4|
|Species|44950|22|
|Condition|Tue Jan 24 00:00:00 UTC 2023|4|
|Family|Fish|file2|
|Species|Bass|8|
|Species|Trout|2|
|Habitat|River|3|
The new files need to be separated based on everything between the Family rows, so for example:
file1.csv
|A|B|C|
|Continent||1|
|Family|44950|file1|
|Species|44950|12|
|Habitat||4|
|Species|44950|22|
|Condition|Tue Jan 24 00:00:00 UTC 2023|4|
file2.csv
|A|B|C|
|Continent||1|
|Family|Fish|file2|
|Species|Bass|8|
|Species|Trout|2|
|Habitat|River|3|
What's the best way of achieving this when the number of rows between appearances of Species is not consistent?
If your file really looks like that ;) then you could use groupby from the standard library module itertools:
from itertools import groupby
def key(line): return line.startswith("|Family|")
family_line, file_no = None, 0
with open("file.csv", "r") as fin:
for is_family_line, lines in groupby(fin, key=key):
if is_family_line:
family_line = list(lines).pop()
elif family_line is None:
header = "".join(lines)
else:
file_no += 1
with open(f"file{file_no}.csv", "w") as fout:
fout.write(header + family_line)
for line in lines:
fout.write(line)
A Pandas solution would be:
import pandas as pd
df = pd.read_csv("file.csv", header=None, delimiter="|").fillna("")
blocks = df.iloc[:, 1].eq("Family").cumsum()
header_df = df[blocks.eq(0)]
for no, sdf in df.groupby(blocks):
if no > 0:
sdf = pd.concat([header_df, sdf])
sdf.to_csv(f"file{no}.csv", index=False, header=False, sep="|")
import pandas as pd
pd.read_csv('file.csv',delimiter='|')
groups = df.groupby('Family')
for name, group in groups:
group.to_csv(name + '.csv', index=False)
Here is a pure python working method:
# Read file
with open('file.csv', 'r') as file:
text = file.read()
# Split using |Family|
splitted_text = text.split("|Family|")
# Remove unwanted content before first |Family|
splitted_text = splitted_text[1:]
# Add |Family| back to each part
splitted_text = ['|Family|' + item for item in splitted_text]
# Write files
for i, content in enumerate(splitted_text ):
with open('file{}.csv'.format(i), 'w') as file:
file.write(content)
I have got multiple csv files which look like this:
ID,Text,Value
1,"I play football",10
2,"I am hungry",12
3,"Unfortunately",I get an error",15
I am currently importing the data using the pandas read_csv() function.
df = pd.read_csv(filename, sep = ',', quotechar='"')
This works for the first two rows in my csv file, unfortunately I get an error in row 3. The reason is that within the 'Text' column there is a quotechar character-comma combination before the end of the column.
ParserError: Error tokenizing data. C error: Expected 3 fields in line 4, saw 4
Is there a way to solve this issue?
Expected output:
ID Text Value
1 I play football 10
2 I am hungry 12
3 Unfortunately, I get an error 15
You can try to fix the CSV using re module:
import re
import pandas as pd
from io import StringIO
with open("your_file.csv", "r") as f_in:
s = re.sub(
r'"(.*)"',
lambda g: '"' + g.group(1).replace('"', "\\") + '"',
f_in.read(),
)
df = pd.read_csv(StringIO(s), sep=r",", quotechar='"', escapechar="\\")
print(df)
Prints:
ID Text Value
0 1 I play football 10
1 2 I am hungry 12
2 3 Unfortunately,I get an error 15
One (not so flexible) approach would be to firstly remove all " quotes from the csv, and then enclose the elements of the specific column with "" quotes(this is done to avoid misinterpreting the "," seperator while parsing), like this:
import csv
# Specify the column index (0-based)
column_index = 1
# Open the input CSV file
with open('input.csv', 'r') as f:
reader = csv.reader(f)
# Open the output CSV file
with open('output.csv', 'w', newline='') as g:
writer = csv.writer(g)
# Iterate through the rows of the input CSV file
for row in reader:
# Replace the " character with an empty string
row[column_index] = row[column_index].replace('"', '')
# Enclose the modified element in "" quotes
row[column_index] = f'"{row[column_index]}"'
# Write the modified row to the output CSV file
writer.writerow(row)
This code creates a new modified csv file
Then your problematic csv row will look like that:
3,"Unfortunately,I get an error",15"
Then you can import the data like you did: df = pd.read_csv(filename, sep = ',', quotechar='"')
To automate this conversion for all csv files within a directory:
import csv
import glob
# Specify the column index (0-based)
column_index = 1
# Get a list of all CSV files in the current directory
csv_files = glob.glob('*.csv')
# Iterate through the CSV files
for csv_file in csv_files:
# Open the input CSV file
with open(csv_file, 'r') as f:
reader = csv.reader(f)
# Open the output CSV file
output_file = csv_file.replace('.csv', '_new.csv')
with open(output_file, 'w', newline='') as g:
writer = csv.writer(g)
# Iterate through the rows of the input CSV file
for row in reader:
# Replace the " character with an empty string
row[column_index] = row[column_index].replace('"', '')
# Enclose the modified element in "" quotes
row[column_index] = f'"{row[column_index]}"'
# Write the modified row to the output CSV file
writer.writerow(row)
this names the new csv files as the old ones but with "_new.csv" instead of just ".csv".
A possible solution:
df = pd.read_csv(filename, sep='(?<=\d),|,(?=\d)', engine='python')
df = df.reset_index().set_axis(['ID', 'Text', 'Value'], axis=1)
df['Text'] = df['Text'].replace('\"', '', regex=True)
Another possible solution:
df = pd.read_csv(StringIO(text), sep='\t')
df[['ID', 'Text']] = df.iloc[:, 0].str.split(',', expand=True, n=1)
df[['Text', 'Value']] = df['Text'].str.rsplit(',', expand=True, n=1)
df = df.drop(df.columns[0], axis=1).assign(
Text=df['Text'].replace('\"', '', regex=True))
Output:
ID Text Value
0 1 I play football 10
1 2 I am hungry 12
2 3 Unfortunately,I get an error 15
So I have a file that looks like this:
name,number,email,job1,job2,job3,job4
I need to convert it to one that looks like this:
name,number,email,job1
name,number,email,job2
name,number,email,job3
name,number,email,job4
How would I do this in Python?
As said in a comment that you can use pandas to read, write and manipulate csv file.
Here is one example of how you can solve your problem with pandas in python
import pandas as pd
# df = pd.read_csv("filename.csv") # read csv file from disk
# comment out below line when open from disk
df = pd.DataFrame([['ss','0152','ss#','student','others']],columns=['name','number','email','job1','job2'])
print(df)
this line output is
name number email job1 job2
0 ss 0152 ss# student others
Now we need to know how many columns are there:
x = len(df.columns)
print(x)
it will store the number of column in x
5
Now let's create a empty Dataframe with columns= [name,number,email,job]
c = pd.DataFrame(columns=['name','number','email','job'])
print(c)
output:
Columns: [name, number, email, job]
Index: []
Now we use loop from range 3 to end of the column and concat datafarme with our empty dataframe:
for i in range(3,x):
df1 = df.iloc[:,0:3].copy() # we took first 3 column
df2 = df.iloc[:,[i]].copy() # we took ith coulmn
df1['job'] = df2; # added ith coulmn to the df1
c = pd.concat([df1,c]); # concat df1 and c
print(c)
output:
name number email job
0 ss 0152 ss# others
0 ss 0152 ss# student
Dataframe c has your desired output. Now you can save it using
c.to_csv('ouput.csv')
Let's assume this is the dataframe:
import pandas as pd
df = pd.DataFrame(columns=['name','number','email','job1','job2','job3','job4'])
df = df.append({'name':'jon', 'number':123, 'email':'smth#smth.smth', 'job1':'a','job2':'b','job3':'c','job4':'d'},ignore_index=True)
We define a new dataframe:
new_df = pd.DataFrame(columns=['name','number','email','job'])
Now, we loop over the old one to split it based on the jobs. I assume you have 4 jobs to split:
for i, row in df.iterrows():
for job in range(1,5):
job_col = "job" + str(job)
new_df = new_df.append({'name':row['name'], 'number':row['number'], 'email':row['email'], 'job':row[job_col]}, ignore_index=True)
You can use the csv module and Python's unpacking syntax to get the data from the input file and write it to the output file.
import csv
with open('input.csv', newline='') as infile, open('output.csv', 'w', newline='') as outfile:
reader = csv.reader(infile)
writer = csv.writer(outfile)
# Skip header row, if necessary
next(reader)
# Use sequence unpacking to get the fixed variables and
# and arbitrary number of "jobs".
for name, number, email, *jobs in reader:
for job in jobs:
writer.writerow([name, number, email, job])
Below:
with open('input.csv') as f_in:
lines = [l.strip() for l in f_in.readlines()]
with open('output.csv','w') as f_out:
for idx,line in enumerate(lines):
if idx > 0:
fields = line.split(',')
for idx in range(3,len(fields)):
f_out.write(','.join(fields[:3]) + ',' + fields[idx] + '\n')
input.csv
header row
name,number,email,job1,job2,job3,job4
name1,number1,email1,job11,job21,job31,job41
output.csv
name,number,email,job1
name,number,email,job2
name,number,email,job3
name,number,email,job4
name1,number1,email1,job11
name1,number1,email1,job21
name1,number1,email1,job31
name1,number1,email1,job41
I have a data frame frame from pandas and now I want to add columns names, but only for the second row. Here is an example of my previous output:
Desired output:
My code:
data_line=open("file1.txt", mode="r")
lines=[]
for line in data_line:
lines.append(line)
for i, line in enumerate(lines):
# print('{}={}'.format(i+1, line.strip()))
file1_header=lines[0]
num_line=1
Dictionary_File1={}
Value_File1= data_type[0:6]
Value_File1_short=[]
i=1
for element in Value_File1:
type=element.split(',')
Value_File1_short.append(type[0] + ", " + type[1] + ", " + type[4])
i += 1
Dictionary_File1[ file1_header]=Value_File1_short
pd_file1=pd.DataFrame.from_dict(Dictionary_File1)
You should have a look at DataFrame.read_csv. The header keyword parameter allows you to indicate a line in the file to use for header names.
You could probably do it with something like:
pd.read_csv("file1.txt", header=1)
From my python shell I tested it out with:
>>> from io import StringIO # I use python3
>>> import pandas as pd
>>> >>> data = """Type Type2 Type3
... A B C
... 1 2 3
... red blue green"""
>>> # StringIO below allows us to use "data" as input to read_csv
>>> # "sep" keyword is used to indicate how columns are separated in data
>>> df = pd.read_csv(StringIO(data), header=1, sep='\s+')
>>> df
A B C
0 1 2 3
1 red blue green
You can write a row using the csv module before writing your dataframe to the same file. Notice this won't help when reading back to Pandas, which doesn't work with "duplicate headers". You can create MultiIndex columns, but this isn't necessary for your desired output.
import pandas as pd
import csv
from io import StringIO
# input file
x = """A,B,C
1,2,3
red,blue,green"""
# replace StringIO(x) with 'file.txt'
df = pd.read_csv(StringIO(x))
with open('file.txt', 'w', newline='') as fout:
writer = csv.writer(fout)
writer.writerow(['Type', 'Type2', 'Type3'])
df.to_csv(fout, index=False)
# read file to check output is correct
df = pd.read_csv('file.txt')
print(df)
# Type Type2 Type3
# 0 A B C
# 1 1 2 3
# 2 red blue green
So if I understand properly, you have a file "file.txt" containing your data, and a list containing the types of your data.
You want to add the list of types, to the pandas.DataFrame of your data. Correct?
If so, you can read the data from the txt file into a pandas.df using pandas.read_csv(), and then define the columns headers using df.columns.
So it would look something like:
df = pd.read_csv("file1.txt", header=None)
df.columns = data_type[0:6]
I hope this helps!
Cheers
i have an excel data that i read in with python pandas:
import pandas as pd
data = pd.read_csv('..../file.txt', sep='\t' )
the mock data looks like this:
unwantedjunkline1
unwantedjunkline2
unwantedjunkline3
ID ColumnA ColumnB ColumnC
1 A B C
2 A B C
3 A B C
...
the data in this case contains 3 junk lines(lines i don't want to read in) before hitting the header and sometimes it contains 4 or more suck junk lines. so in this case i read in the data :
data = pd.read_csv('..../file.txt', sep='\t', skiprows = 3 )
data looks like:
ID ColumnA ColumnB ColumnC
1 A B C
2 A B C
3 A B C
...
But each time the number of unwanted lines is different, is there a way to read in a table file using pandas without using 'skiprows=' but instead using some command that matches the header so it knows to start reading from the header? so I don't have to click open the file to count how many unwanted lines the file contains each time and then manually change the 'skiprows=' option.
If you know what the header startswith:
def skip_to(fle, line,**kwargs):
if os.stat(fle).st_size == 0:
raise ValueError("File is empty")
with open(fle) as f:
pos = 0
cur_line = f.readline()
while not cur_line.startswith(line):
pos = f.tell()
cur_line = f.readline()
f.seek(pos)
return pd.read_csv(f, **kwargs)
Demo:
In [18]: cat test.txt
1,2
3,4
The,header
foo,bar
foobar,foo
In [19]: df = skip_to("test.txt","The,header", sep=",")
In [20]: df
Out[20]:
The header
0 foo bar
1 foobar foo
By calling .tell we keep track of where the pointer is for the previous line so when we hit the header we seek back to that line and just pass the file object to pandas.
Or using the junk if they all started with something in common:
def skip_to(fle, junk,**kwargs):
if os.stat(fle).st_size == 0:
raise ValueError("File is empty")
with open(fle) as f:
pos = 0
cur_line = f.readline()
while cur_line.startswith(junk):
pos = f.tell()
cur_line = f.readline()
f.seek(pos)
return pd.read_csv(f, **kwargs)
df = skip_to("test.txt", "junk",sep="\t")
Another simple way to achieve a dynamic skiprows would something like this which worked for me:
# Open the file
with open('test.csv', encoding='utf-8') as readfile:
ls_readfile = readfile.readlines()
#Find the skiprows number with ID as the startswith
skip = next(filter(lambda x: x[1].startswith('ID'), enumerate(ls_readfile)))[0]
print(skip)
#import the file with the separator \t
df = pd.read_csv(r'test.txt', skiprows=skip, sep ='\t')