I have a CSV file looks like
Hit39, Hit24, Hit9
Hit8, Hit39, Hit21
Hit46, Hit47, Hit20
Hit24, Hit 53, Hit46
I want to read file and create a dictionary based on the first come first serve first basis
like Hit39 : 1, Hit 24:2 and so on ...
but notice Hit39 appeared on column 2 and row2 . So if the reader reads it then it should not append it to dictionary it will move on with the new number.
Once a row number is visited it shouldn't include numbers after that if appeared.
Using Python - Best guess until the OP is clarified - treat the file as though it was one huge list and assign an incrementing variable to unique occurences of value.
import csv
from itertools import count
mydict = {}
counter = count(1)
with open('infile.csv') as fin:
for row in csv.reader(fin, skipinitialspace=True):
for col in row:
mydict[col] = mydict.get(col, next(counter))
Since Python is a popular language that has dictionaries, you must be using Python. At least I assume.
import csv
reader = csv.reader(file("filename.csv"))
d = dict((line[0], 1+lineno) for lineno, line in enumerate(reader))
print d
Related
I am currently trying to import a big csv file (50GB+) without any headers into a pyarrow table with the overall target to export this file into the Parquet format and further to process it in a Pandas or Dask DataFrame. How can i specify the column names and column dtypes within pyarrow for the csv file?
I already thought about to append the header to the csv file. This enforces a complete rewrite of the file which looks like a unnecssary overhead. As far as I know, pyarrow provides schemas to define the dtypes for specific columns, but the docs are missing a concrete example for doing so while transforming a csv file to an arrow table.
Imagine that this csv file just has for an easy example the two columns "A" and "B".
My current code looks like this:
import numpy as np
import pandas as pd
import pyarrow as pa
df_with_header = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
print(df_with_header)
df_with_header.to_csv("data.csv", header=False, index=False)
df_without_header = pd.read_csv('data.csv', header=None)
print(df_without_header)
opts = pa.csv.ConvertOptions(column_types={'A': 'int8',
'B': 'int8'})
table = pa.csv.read_csv(input_file = "data.csv", convert_options = opts)
print(table)
If I print out the final table, its not going to change the names of the columns.
pyarrow.Table
1: int64
3: int64
How can I now change the loaded column names and dtypes? Is there maybe also a possibility to for example pass in a dict containing the names and their dtypes?
You can specify type overrides for columns:
fp = io.BytesIO(b'one,two,three\n1,2,3\n4,5,6')
fp.seek(0)
table = csv.read_csv(
fp,
convert_options=csv.ConvertOptions(
column_types={
'one': pa.int8(),
'two': pa.int8(),
'three': pa.int8(),
}
))
But in your case you don't have a header, and as far as I can tell this use case is not supported in arrow:
fp = io.BytesIO(b'1,2,3\n4,5,6')
fp.seek(0)
table = csv.read_csv(
fp,
parse_options=csv.ParseOptions(header_rows=0)
)
This raises:
pyarrow.lib.ArrowInvalid: header_rows == 0 needs explicit column names
The code is here: https://github.com/apache/arrow/blob/3cf8f355e1268dd8761b99719ab09cc20d372185/cpp/src/arrow/csv/reader.cc#L138
This is similar to this question apache arrow - reading csv file
There should be fix for it in the next version: https://github.com/apache/arrow/pull/4898
I am trying to develop a program using Python3.6.4 which convert a JSON file into a CSV file and also we need to clean the data in the csv file. as for example:
My JSON File:
{emp:[{"Name":"Bo#b","email":"bob#gmail.com","Des":"Unknown"},
{"Name":"Martin","email":"mar#tin#gmail.com","Des":"D#eveloper"}]}
Problem 1:
After converting that into csv its creating a blank row between every 2 rows. As
**Name email Des**
[<BLANK ROW>]
Bo#b bob#gmail.com Unknown
[<BLANK ROW>]
Martin mar#tin#gmail.com D#eveloper
Problem 2:
In my code I am using emp but I need to use it dynamically.
fobj = open("D:/Users/shamiks/PycharmProjects/jsonSamle.txt")
jsonCont = fobj.read()
print(jsonCont)
fobj.close()
employee_parsed = json.loads(jsonCont)
emp_data = employee_parsed['employee']
As we will not know the structure or content of up-coming JSON file.
Problem 3:
I also need to remove all # characters from the CSV file.
For solving Problem 3, you can use .replace (https://www.tutorialspoint.com/python/string_replace.htm).
For problem 2, you can use the dictionary keys and then get the zeroth item out of it.
fobj = open("D:/Users/shamiks/PycharmProjects/jsonSamle.txt")
jsonCont = fobj.read().replace("#", "")
print(jsonCont)
fobj.close()
employee_parsed = json.loads(jsonCont)
first_key = employee_parsed.keys()[0]
emp_data = employee_parsed[first_key]
I can't solve problem 1 without more code to see how your are exporting the result. It may be that your data has newlines in it. In which case, you could add .replace("\n","") and/or .replace("\r","") after the previous replace so the line would read fobj.read().replace("#", "").replace("\n", "").replace("\r", "").
I am trying to use Spark to read a csv file in jupyter notebook. So far I have
spark = SparkSession.builder.master("local[4]").getOrCreate()
reviews_df = spark.read.option("header","true").csv("small.csv")
reviews_df.collect()
This is how the reviews_df looks like:
[Row(reviewerID=u'A1YKOIHKQHB58W', asin=u'B0001VL0K2', overall=u'5'),
Row(reviewerID=u'A2YB0B3QOHEFR', asin=u'B000JJSRNY', overall=u'5'),
Row(reviewerID=u'AAI0092FR8V1W', asin=u'B0060MYKYY', overall=u'5'),
Row(reviewerID=u'A2TAPSNKK9AFSQ', asin=u'6303187218', overall=u'5'),
Row(reviewerID=u'A316JR2TQLQT5F', asin=u'6305364206', overall=u'5')...]
But each row of the data frame contains the column names, how can I reformat the data, so that it can become:
[(u'A1YKOIHKQHB58W', u'B0001VL0K2', u'5'),
(u'A2YB0B3QOHEFR', u'B000JJSRNY', u'5')....]
Dataframe always returns Row objects, thats why when you issue collect() on dataframe, it shows -
Row(reviewerID=u'A1YKOIHKQHB58W', asin=u'B0001VL0K2', overall=u'5')
to get what you want, you can do -
reviews_df.rdd.map(lambda row : (row.reviewerID,row.asin,row.overall)).collect()
this will return you tuple of values of rows
I have a cvs file with data looking like:
lastname firstname id
segre alberto 14562
I want to just read in the column with the id numbers
everything I try keeps giving me the line not the column of the csv file
import csv
import operator
idgetter = operator.itemgetter(2)
with open('path/to/file') as infile:
infile.readline()
ids = [idgetter(row) for row in csv.reader(infile)]
You could use Pandas.
import pandas as pd
col = pd.read_csv('/path/to/file/')['id']
And if you want it as a list, simply list(col) will do the trick.
BACKGROUND:
I am having issues trying to search through some CSV files.
I've gone through the python documentation: http://docs.python.org/2/library/csv.html
about the csv.DictReader(csvfile, fieldnames=None, restkey=None, restval=None, dialect='excel', *args, **kwds) object of the csv module.
My understanding is that the csv.DictReader assumes the first line/row of the file are the fieldnames, however, my csv dictionary file simply starts with "key","value" and goes on for atleast 500,000 lines.
My program will ask the user for the title (thus the key) they are looking for, and present the value (which is the 2nd column) to the screen using the print function. My problem is how to use the csv.dictreader to search for a specific key, and print its value.
Sample Data:
Below is an example of the csv file and its contents...
"Mamer","285713:13"
"Champhol","461034:2"
"Station Palais","972811:0"
So if i want to find "Station Palais" (input), my output will be 972811:0. I am able to manipulate the string and create the overall program, I just need help with the csv.dictreader.I appreciate any assistance.
EDITED PART:
import csv
def main():
with open('anchor_summary2.csv', 'rb') as file_data:
list_of_stuff = []
reader = csv.DictReader(file_data, ("title", "value"))
for i in reader:
list_of_stuff.append(i)
print list_of_stuff
main()
The documentation you linked to provides half the answer:
class csv.DictReader(csvfile, fieldnames=None, restkey=None, restval=None, dialect='excel', *args, **kwds)
[...] maps the information read into a dict whose keys are given by the optional fieldnames parameter. If the fieldnames parameter is omitted, the values in the first row of the csvfile will be used as the fieldnames.
It would seem that if the fieldnames parameter is passed, the given file will not have its first record interpreted as headers (the parameter will be used instead).
# file_data is the text of the file, not the filename
reader = csv.DictReader(file_data, ("title", "value"))
for i in reader:
list_of_stuff.append(i)
which will (apparently; I've been having trouble with it) produce the following data structure:
[{"title": "Mamer", "value": "285713:13"},
{"title": "Champhol", "value": "461034:2"},
{"title": "Station Palais", "value": "972811:0"}]
which may need to be further massaged into a title-to-value mapping by something like this:
data = {}
for i in list_of_stuff:
data[i["title"]] = i["value"]
Now just use the keys and values of data to complete your task.
And here it is as a dictionary comprehension:
data = {row["title"]: row["value"] for row in csv.DictReader(file_data, ("title", "value"))}
The currently accepted answer is fine, but there's a slightly more direct way of getting at the data. The dict() constructor in Python can take any iterable.
In addition, your code might have issues on Python 3, because Python 3's csv module expects the file to be opened in text mode, not binary mode. You can make your code compatible with 2 and 3 by using io.open instead of open.
import csv
import io
with io.open('anchor_summary2.csv', 'r', newline='', encoding='utf-8') as f:
data = dict(csv.reader(f))
print(data['Champhol'])
As a warning, if your csv file has two rows with the same value in the first column, the later value will overwrite the earlier value. (This is also true of the other posted solution.)
If your program really is only supposed to print the result, there's really no reason to build a keyed dictionary.
import csv
import io
# Python 2/3 compat
try:
input = raw_input
except NameError:
pass
def main():
# Case-insensitive & leading/trailing whitespace insensitive
user_city = input('Enter a city: ').strip().lower()
with io.open('anchor_summary2.csv', 'r', newline='', encoding='utf-8') as f:
for city, value in csv.reader(f):
if user_city == city.lower():
print(value)
break
else:
print("City not found.")
if __name __ == '__main__':
main()
The advantage of this technique is that the csv isn't loaded into memory and the data is only iterated over once. I also added a little code the calls lower on both the keys to make the match case-insensitive. Another advantage is if the city the user requests is near the top of the file, it returns almost immediately and stops looking through the file.
With all that said, if searching performance is your primary consideration, you should consider storing the data in a database.