I have lists like this:
['7801234567', 'Robert Post', '66 Hinton Road']
['7809876543', 'Farrukh Ahmed', '101 Edson Crest']
['7803214567', 'Md Toukir Imam', '34 Sherwood Park Avenue']
['7807890123', 'Elham Ahmadi', '8 Devon Place']
['7808907654', 'Rong Feng', '32 Spruce Street']
['7801236789', 'Nazanin Tahmasebi', '98 Albert Avenue']
['7804321098', 'Sayem Mohammad Siam', '56 Stony Place']
['7808765432', 'Amir Hossein Faghih Dinevari', '45 Beautiful Street']
How should I convert them to dictionaries with keys named: 'tel', 'name', and'address'?
Use zip to associate the keys with their values by index
keys = ('tel', 'name', 'address')
values = ['7801234567', 'Robert Post', '66 Hinton Road']
d = dict(zip(keys, values))
# {'tel': '7801234567', 'name': 'Robert Post', 'address': '66 Hinton Road'}
Edit:
Obviously, you can use this technique to create more complex structures.
info = [['7801234567', 'Robert Post', '66 Hinton Road'],
['7809876543', 'Farrukh Ahmed', '101 Edson Crest'],
['7803214567', 'Md Toukir Imam', '34 Sherwood Park Avenue'],
['7807890123', 'Elham Ahmadi', '8 Devon Place'],
['7808907654', 'Rong Feng', '32 Spruce Street'],
['7801236789', 'Nazanin Tahmasebi', '98 Albert Avenue'],
['7804321098', 'Sayem Mohammad Siam', '56 Stony Place'],
['7808765432', 'Amir Hossein Faghih Dinevari', '45 Beautiful Street']]
keys = ('tel', 'name', 'address')
dictionaries = [dict(zip(keys, values)) for values in info]
The [_ for _ in _] is called a list comprehension
We assume the data are given as:
info_row = [['7801234567', 'Robert Post', '66 Hinton Road'],
['7809876543', 'Farrukh Ahmed', '101 Edson Crest'],
['7803214567', 'Md Toukir Imam', '34 Sherwood Park Avenue'],
...,
]
and
info_type = ['tel', 'name', 'address']
We could first write a function to convert each "row" list.
def convert_row(row_name, row_content):
d = dict()
for i, j in enumerate(row_name):
d[j] = row_content[i]
return d
Then, we can use list comprehension to apply such function to the whole list of list.
expected_result = [convert_row(info_type, r) for r in info_row]
As another answer has pointed out, we can make use of zip. Using built-in routines is more than encouraged in most cases. So it is better to type
expected_result = [dict(zip(info_type, r)) for r in info_row]
You can do this in a list comprehension:
data = [['7801234567', 'Robert Post', '66 Hinton Road'],
['7809876543', 'Farrukh Ahmed', '101 Edson Crest'],
['7803214567', 'Md Toukir Imam', '34 Sherwood Park Avenue'],
['7807890123', 'Elham Ahmadi', '8 Devon Place'],
['7808907654', 'Rong Feng', '32 Spruce Street'],
['7801236789', 'Nazanin Tahmasebi', '98 Albert Avenue'],
['7804321098', 'Sayem Mohammad Siam', '56 Stony Place'],
['7808765432', 'Amir Hossein Faghih Dinevari', '45 Beautiful Street']]
new_data = [{'tel': tel, 'name': name, 'address': address} for tel, name, address in data]
print(new_data)
Which Outputs:
[{'tel': '7801234567', 'name': 'Robert Post', 'address': '66 Hinton Road'}, {'tel': '7809876543', 'name': 'Farrukh Ahmed', 'address': '101 Edson Crest'}, {'tel': '7803214567', 'name': 'Md Toukir Imam', 'address': '34 Sherwood Park Avenue'}, {'tel': '7807890123', 'name': 'Elham Ahmadi', 'address': '8 Devon Place'}, {'tel': '7808907654', 'name': 'Rong Feng', 'address': '32 Spruce Street'}, {'tel': '7801236789', 'name': 'Nazanin Tahmasebi', 'address': '98 Albert Avenue'}, {'tel': '7804321098', 'name': 'Sayem Mohammad Siam', 'address': '56 Stony Place'}, {'tel': '7808765432', 'name': 'Amir Hossein Faghih Dinevari', 'address': '45 Beautiful Street'}]
Related
Start with a, and ends with a. I have been trying to output capital cities that start and end with the letter "a". Doesn't matter if they start with capital "A"
capitals = ('Kabul', 'Tirana (Tirane)', 'Algiers', 'Andorra la Vella', 'Luanda', "Saint John's", 'Buenos Aires', 'Yerevan', 'Canberra', 'Vienna', 'Baku', 'Nassau', 'Manama', 'Dhaka', 'Bridgetown', 'Minsk', 'Brussels', 'Belmopan', 'Porto Novo', 'Thimphu', 'Sucre', 'Sarajevo', 'Gaborone', 'Brasilia', 'Bandar Seri Begawan', 'Sofia', 'Ouagadougou', 'Gitega', 'Phnom Penh', 'Yaounde', 'Ottawa', 'Praia', 'Bangui', "N'Djamena", 'Santiago', 'Beijing', 'Bogota', 'Moroni', 'Kinshasa', 'Brazzaville', 'San Jose', 'Yamoussoukro', 'Zagreb', 'Havana', 'Nicosia', 'Prague', 'Copenhagen', 'Djibouti', 'Roseau', 'Santo Domingo', 'Dili', 'Quito', 'Cairo', 'San Salvador', 'London', 'Malabo', 'Asmara', 'Tallinn', 'Mbabana', 'Addis Ababa', 'Palikir', 'Suva', 'Helsinki', 'Paris', 'Libreville', 'Banjul', 'Tbilisi', 'Berlin', 'Accra', 'Athens', "Saint George's", 'Guatemala City', 'Conakry', 'Bissau', 'Georgetown', 'Port au Prince', 'Tegucigalpa', 'Budapest', 'Reykjavik', 'New Delhi', 'Jakarta', 'Tehran', 'Baghdad', 'Dublin', 'Jerusalem', 'Rome', 'Kingston', 'Tokyo', 'Amman', 'Nur-Sultan', 'Nairobi', 'Tarawa Atoll', 'Pristina', 'Kuwait City', 'Bishkek', 'Vientiane', 'Riga', 'Beirut', 'Maseru', 'Monrovia', 'Tripoli', 'Vaduz', 'Vilnius', 'Luxembourg', 'Antananarivo', 'Lilongwe', 'Kuala Lumpur', 'Male', 'Bamako', 'Valletta', 'Majuro', 'Nouakchott', 'Port Louis', 'Mexico City', 'Chisinau', 'Monaco', 'Ulaanbaatar', 'Podgorica', 'Rabat', 'Maputo', 'Nay Pyi Taw', 'Windhoek', 'No official capital', 'Kathmandu', 'Amsterdam', 'Wellington', 'Managua', 'Niamey', 'Abuja', 'Pyongyang', 'Skopje', 'Belfast', 'Oslo', 'Muscat', 'Islamabad', 'Melekeok', 'Panama City', 'Port Moresby', 'Asuncion', 'Lima', 'Manila', 'Warsaw', 'Lisbon', 'Doha', 'Bucharest', 'Moscow', 'Kigali', 'Basseterre', 'Castries', 'Kingstown', 'Apia', 'San Marino', 'Sao Tome', 'Riyadh', 'Edinburgh', 'Dakar', 'Belgrade', 'Victoria', 'Freetown', 'Singapore', 'Bratislava', 'Ljubljana', 'Honiara', 'Mogadishu', 'Pretoria, Bloemfontein, Cape Town', 'Seoul', 'Juba', 'Madrid', 'Colombo', 'Khartoum', 'Paramaribo', 'Stockholm', 'Bern', 'Damascus', 'Taipei', 'Dushanbe', 'Dodoma', 'Bangkok', 'Lome', "Nuku'alofa", 'Port of Spain', 'Tunis', 'Ankara', 'Ashgabat', 'Funafuti', 'Kampala', 'Kiev', 'Abu Dhabi', 'London', 'Washington D.C.', 'Montevideo', 'Tashkent', 'Port Vila', 'Vatican City', 'Caracas', 'Hanoi', 'Cardiff', "Sana'a", 'Lusaka', 'Harare')
This is my code:
for elem in capitals:
elem = elem.lower()
["".join(j for j in i if j not in string.punctuation) for i in capitals]
if (len(elem) >=4 and elem.endswith(elem[0])):
print(elem)
My output is:
andorra la vella
saint john's
asmara
addis ababa
accra
saint george's
nur-sultan
abuja
oslo
warsaw
apia
ankara
tashkent
My expected output is:
andorra la vella
asmara
addis ababa
accra
abuja
apia
ankara
You didn't check if the capital starts with 'a'. I also assumed you want to filter out punctuation based on your code, so this is what I ended up with:
import string
for elem in capitals:
elem = elem.lower()
for punct in string.punctuation:
elem = elem.replace(punct, '')
if elem.startswith('a') and elem.endswith('a'):
print(elem)
for elem in capitals:
elem = elem.lower()
if (elem.startswith('a') and elem.endswith('a')):
print(elem)
If I have a data-frame of 2000 and in which let say brand have 142 unique values and i want to count frequency of every unique value form 1 to 142.values should change dynamically.
brand=clothes_z.brand_name
brand.describe(include="all")
unique_brand=brand.unique()
brand.describe(include="all"),unique_brand
Output:
(count 2613
unique 142
top Mango
freq 54
Name: brand_name, dtype: object,
array(['Jack & Jones', 'TOM TAILOR DENIM', 'YOURTURN', 'Tommy Jeans',
'Alessandro Zavetti', 'adidas Originals', 'Volcom', 'Pier One',
'Superdry', 'G-Star', 'SIKSILK', 'Tommy Hilfiger', 'Karl Kani',
'Alpha Industries', 'Farah', 'Nike Sportswear',
'Calvin Klein Jeans', 'Champion', 'Hollister Co.', 'PULL&BEAR',
'Nike Performance', 'Even&Odd', 'Stradivarius', 'Mango',
'Champion Reverse Weave', 'Massimo Dutti', 'Selected Femme Petite',
'NAF NAF', 'YAS', 'New Look', 'Missguided', 'Miss Selfridge',
'Topshop', 'Miss Selfridge Petite', 'Guess', 'Esprit Collection',
'Vero Moda', 'ONLY Petite', 'Selected Femme', 'ONLY', 'Dr.Denim',
'Bershka', 'Vero Moda Petite', 'PULL & BEAR', 'New Look Petite',
'JDY', 'Even & Odd', 'Vila', 'Lacoste', 'PS Paul Smith',
'Redefined Rebel', 'Selected Homme', 'BOSS', 'Brave Soul', 'Mind',
'Scotch & Soda', 'Only & Sons', 'The North Face',
'Polo Ralph Lauren', 'Gym King', 'Selected Woman', 'Rich & Royal',
'Rooms', 'Glamorous', 'Club L London', 'Zalando Essentials',
'edc by Esprit', 'OYSHO', 'Oasis', 'Gina Tricot',
'Glamorous Petite', 'Cortefiel', 'Missguided Petite',
'Missguided Tall', 'River Island', 'INDICODE JEANS',
'Kings Will Dream', 'Topman', 'Esprit', 'Diesel', 'Key Largo',
'Mennace', 'Lee', "Levi's®", 'adidas Performance', 'jordan',
'Jack & Jones PREMIUM', 'They', 'Springfield', 'Benetton', 'Fila',
'Replay', 'Original Penguin', 'Kronstadt', 'Vans', 'Jordan',
'Apart', 'New look', 'River island', 'Freequent', 'Mads Nørgaard',
'4th & Reckless', 'Morgan', 'Honey punch', 'Anna Field Petite',
'Noisy may', 'Pepe Jeans', 'Mavi', 'mint & berry', 'KIOMI', 'mbyM',
'Escada Sport', 'Lost Ink', 'More & More', 'Coffee', 'GANT',
'TWINTIP', 'MAMALICIOUS', 'Noisy May', 'Pieces', 'Rest',
'Anna Field', 'Pinko', 'Forever New', 'ICHI', 'Seafolly', 'Object',
'Freya', 'Wrangler', 'Cream', 'LTB', 'G-star', 'Dorothy Perkins',
'Carhartt WIP', 'Betty & Co', 'GAP', 'ONLY Tall', 'Next', 'HUGO',
'Violet by Mango', 'WEEKEND MaxMara', 'French Connection'],
dtype=object))
As it is showing only frequency of Mango "54" because it is top frequency and I want every value frequency like what is the frequency of Jack & Jones, TOM TAILOR DENIM and YOURTURN and so on... and values should change dynamically.
You could simply do,
clothes_z.brand_name.value_counts()
This would list down the unique values and would give you the frequency of every element in that Pandas Series.
from collections import Counter
ll = [...your list of brands...]
c = Counter(ll)
# you can do whatever you want with your counted values
df = pd.DataFrame.from_dict(c, orient='index', columns=['counted'])
I want function input is list's and each list save as one row.
My attempt to save a two-dimensional array:
my_list = [['\ufeffUser Name', 'First Name', 'Last Name', 'Display Name', 'Job Title', 'Department', 'Office Number', 'Office Phone', 'Mobile Phone', 'Fax', 'Address', 'City', 'State or Province', 'ZIP or Postal Code', 'Country or Region'], ['chris#contoso.com', 'Chris', 'Green', 'Chris Green', 'IT Manager', 'Information Technology', '123451', '123-555-1211', '123-555-6641', '123-555-9821', '1 Microsoft way', 'Redmond', 'Wa',
'98052', 'United States'], ['ben#contoso.com', 'Ben', 'Andrews', 'Ben Andrews', 'IT Manager', 'Information Technology', '123452', '123-555-1212', '123-555-6642', '123-555-9822', '1 Microsoft way', 'Redmond', 'Wa', '98052', 'United States'], ['david#contoso.com', 'David', 'Longmuir', 'David Longmuir', 'IT Manager', 'Information Technology', '123453', '123-555-1213', '123-555-6643', '123-555-9823', '1 Microsoft way', 'Redmond', 'Wa', '98052', 'United States'], ['cynthia#contoso.com', 'Cynthia', 'Carey', 'Cynthia Carey', 'IT Manager', 'Information Technology', '123454', '123-555-1214', '123-555-6644', '123-555-9824', '1 Microsoft way', 'Redmond', 'Wa', '98052', 'United States'], ['melissa#contoso.com', 'Melissa', 'MacBeth', 'Melissa MacBeth', 'IT Manager', 'Information Technology', '123455', '123-555-1215', '123-555-6645', '123-555-9825', '1 Microsoft way', 'Redmond', 'Wa', '98052', 'United States']]
unifile.dump.excel("file.csv", "array", "utf-8", my_list)
My attempt to save lists to CSV files:
l1 = ["aa", "ftyg"]
l2 = ["fgghg", "ftyfuv"]
unifile.dump.excel("file.csv", "list", "utf-8", l1, l2)
My function
def excel(file_path: str, mode: str = "array", t_encoding: str = "utf-8", write_mode: str = "r", *data: list):
try:
os.remove(file_path)
except:
pass
if mode == "list":
with open(file_path, 'w') as csv_file:
csv_writer = csv.writer(csv_file, delimiter=',')
for l in data:
csv_writer.writerow(l)
elif mode == "array":
with open(file_path, write_mode, encoding=t_encoding) as csv_file:
csv_writer = csv.writer(csv_file, delimiter=',')
for l in data:
csv_writer.writerow(l)
else:
pass
User Name,First Name,Last Name,Display Name,Job Title,Department,Office Number,Office Phone,Mobile Phone,Fax,Address,City,State or Province,ZIP or Postal Code,Country or Region
chris#contoso.com,Chris,Green,Chris Green,IT Manager,Information Technology,123451,123-555-1211,123-555-6641,123-555-9821,1 Microsoft way,Redmond,Wa,98052,United States
ben#contoso.com,Ben,Andrews,Ben Andrews,IT Manager,Information Technology,123452,123-555-1212,123-555-6642,123-555-9822,1 Microsoft way,Redmond,Wa,98052,United States
david#contoso.com,David,Longmuir,David Longmuir,IT Manager,Information Technology,123453,123-555-1213,123-555-6643,123-555-9823,1 Microsoft way,Redmond,Wa,98052,United States
cynthia#contoso.com,Cynthia,Carey,Cynthia Carey,IT Manager,Information Technology,123454,123-555-1214,123-555-6644,123-555-9824,1 Microsoft way,Redmond,Wa,98052,United States
melissa#contoso.com,Melissa,MacBeth,Melissa MacBeth,IT Manager,Information Technology,123455,123-555-1215,123-555-6645,123-555-9825,1 Microsoft way,Redmond,Wa,98052,United States
The problem is this code will replace all previous lines with end line.
The Pandas module is really useful. This worked for me:
import pandas as pd
my_list = pd.DataFrame(my_list)
my_list.to_csv("example.csv")
Extending an existing question and answer here, I am trying to extract player name and his position. The output would like:
playername, position
EJ Manuel, Quarterbacks
Tyrod Taylor, Quarterbacks
Anthony Dixon, Running backs
...
This is what I have done so far:
tree = html.fromstring(requests.get("https://en.wikipedia.org/wiki/List_of_current_AFC_team_rosters").text)
for h3 in tree.xpath("//table[#class='toccolours']//tr[2]"):
position = h3.xpath(".//b/text()")
players = h3.xpath(".//ul/li/a/text()")
print(position, players)
The above codes can deliver the following, but not in the format I need.
(['Quarterbacks', 'Running backs', 'Wide receivers', 'Tight ends', 'Offensive linemen', 'Defensive linemen', 'Linebackers', 'Defensive backs', 'Special teams', 'Reserve lists', 'Unrestricted FAs', 'Restricted FAs', 'Exclusive-Rights FAs'], ['EJ Manuel', 'Tyrod Taylor', 'Anthony Dixon', 'Jerome Felton', 'Mike Gillislee', 'LeSean McCoy', 'Karlos Williams', 'Leonard Hankerson', 'Marcus Easley', 'Marquise Goodwin', 'Percy Harvin', 'Dez Lewis', 'Walt Powell', 'Greg Salas', 'Sammy Watkins', 'Robert Woods', 'Charles Clay', 'Chris Gragg', "Nick O'Leary", 'Tyson Chandler', 'Ryan Groy', 'Seantrel Henderson', 'Cyrus Kouandjio', 'John Miller', 'Kraig Urbik', 'Eric Wood', 'T. J. Barnes', 'Marcell Dareus', 'Lavar Edwards', 'IK Enemkpali', 'Jerry Hughes', 'Kyle Williams', 'Mario Williams', 'Jerel Worthy', 'Jarius Wynn', 'Preston Brown', 'Randell Johnson', 'Manny Lawson', 'Kevin Reddick', 'Tony Steward', 'A. J. Tarpley', 'Max Valles', 'Mario Butler', 'Ronald Darby', 'Stephon Gilmore', 'Corey Graham', 'Leodis McKelvin', 'Jonathan Meeks', 'Merrill Noel', 'Nickell Robey', 'Sammy Seamster', 'Cam Thomas', 'Aaron Williams', 'Duke Williams', 'Dan Carpenter', 'Jordan Gay', 'Garrison Sanborn', 'Colton Schmidt', 'Blake Annen', 'Jarrett Boykin', 'Jonathan Dowling', 'Greg Little', 'Jacob Maxwell', 'Ronald Patrick', 'Cedric Reed', 'Cyril Richardson', 'Phillip Thomas', 'James Wilder, Jr.', 'Nigel Bradham', 'Ron Brooks', 'Alex Carrington', 'Cordy Glenn', 'Leonard Hankerson', 'Richie Incognito', 'Josh Johnson', 'Corbin Bryant', 'Stefan Charles', 'MarQueis Gray', 'Chris Hogan', 'Jordan Mills', 'Ty Powell', 'Bacarri Rambo', 'Cierre Wood'])
(['Quarterbacks', 'Running backs', 'Wide receivers', 'Tight ends', 'Offensive linemen', 'Defensive linemen', 'Linebackers', 'Defensive backs', 'Special teams', 'Reserve lists', 'Unrestricted FAs', 'Restricted FAs', 'Exclusive-Rights FAs'], ['Zac Dysert', 'Ryan Tannehill', 'Logan Thomas', 'Jay Ajayi', 'Jahwan Edwards', 'Damien Williams', 'Tyler Davis', 'Robert Herron', 'Greg Jennings', 'Jarvis Landry', 'DeVante Parker', 'Kenny Stills', 'Jordan Cameron', 'Dominique Jones', 'Dion Sims', 'Branden Albert', 'Jamil Douglas', "Ja'Wuan James", 'Vinston Painter', 'Mike Pouncey', 'Anthony Steen', 'Dallas Thomas', 'Billy Turner', 'Deandre Coleman', 'Quinton Coples', 'Terrence Fede', 'Dion Jordan', 'Earl Mitchell', 'Damontre Moore', 'Jordan Phillips', 'Ndamukong Suh', 'Charles Tuaau', 'Robert Thomas', 'Cameron Wake', 'Julius Warmsley', 'Jordan Williams', 'Neville Hewitt', 'Mike Hull', 'Jelani Jenkins', 'Terrell Manning', 'Chris McCain', 'Koa Misi', 'Zach Vigil', 'Walt Aikens', 'Damarr Aultman', 'Brent Grimes', 'Reshad Jones', 'Tony Lippett', 'Bobby McCain', 'Brice McCain', 'Tyler Patmon', 'Dax Swanson', 'Jamar Taylor', 'Matt Darr', 'John Denney', 'Andrew Franks', 'Louis Delmas', 'James-Michael Johnson', 'Rishard Matthews', 'Jacques McClendon', 'Lamar Miller', 'Matt Moore', 'Spencer Paysinger', 'Derrick Shelby', 'Kelvin Sheppard', 'Shelley Smith', 'Olivier Vernon', 'Michael Thomas', 'Brandon Williams', 'Shamiel Gary', 'Matt Hazel', 'Ulrick John', 'Jake Stoneburner'])
...
Any suggestions?
You can use nested loop for this task. First loop through the positions and then, for each position, loop through the corresponding players :
#loop through positions
for b in tree.xpath("//table[#class='toccolours']//tr[2]//b"):
#get current position text
position = b.xpath("text()")[0]
#get players that correspond to the current position
for a in b.xpath("following::ul[1]/li/a[not(*)]"):
#get current player text
player = a.xpath("text()")[0]
#print current position and player together
print(position, player)
Last part of the output :
.....
('Reserve lists', 'Chris Watt')
('Reserve lists', 'Eric Weddle')
('Reserve lists', 'Tourek Williams')
('Practice squad', 'Alex Bayer')
('Practice squad', 'Isaiah Burse')
('Practice squad', 'Richard Crawford')
('Practice squad', 'Ben Gardner')
('Practice squad', 'Michael Huey')
('Practice squad', 'Keith Lewis')
('Practice squad', 'Chuka Ndulue')
('Practice squad', 'Tim Semisch')
('Practice squad', 'Brad Sorensen')
('Practice squad', 'Craig Watts')
I have a CSV file, which contains couple of columns. For Example :
FName,LName,Address1,City,Country,Phone,Email
Matt,Shew,"503, Avenue Park",Auckland,NZ,19809224478,matt#xxx.com
Patt,Smith,"503, Baker Street
Mickey Park
Suite 510",Austraila,AZ,19807824478,patt#xxx.com
Doug,Stew,"12, Main St.
21st Lane
Suit 290",Chicago,US,19809224478,doug#xxx.com
Henry,Mark,"88, Washington Park",NY,US,19809224478,matt#xxx.com
In excel it looks something likes this :
It's a usual human tendency to feed/copy-paste address in the particular manner, usually sometimes people copy their signature and paste it to the Address column which creates such situation.
I have tried reading this using Python CSV module and it looks like that python doesn't distinguish between the '\n' Newline between the field values and the end of line.
My code :
import csv
with open(file_path, 'r') as f_obj:
input_data = []
reader = csv.DictReader(f_obj)
for row in reader:
print row
The output looks somethings like this :
{'City': 'Auckland', 'Address1': '503, Avenue Park', 'LName': 'Shew', 'Phone': '19809224478', 'FName': 'Matt', 'Country': 'NZ', 'Email': 'matt#xxx.com'}
{'City': 'Austraila', 'Address1': '503, Baker Street\nMickey Park\nSuite 510', 'LName': 'Smith', 'Phone': '19807824478', 'FName': 'Patt', 'Country': 'AZ', 'Email': 'patt#xxx.com'}
{'City': 'Chicago', 'Address1': '12, Main St. \n21st Lane \nSuit 290', 'LName': 'Stew', 'Phone': '19809224478', 'FName': 'Doug', 'Country': 'US', 'Email': 'doug#xxx.com'}
{'City': 'NY', 'Address1': '88, Washington Park', 'LName': 'Mark', 'Phone': '19809224478', 'FName': 'Henry', 'Country': 'US', 'Email': 'matt#xxx.com'}
I just wanted to write the same content to a file where all the values for a Address1 keys should not have '\n' character and looks like :
{'City': 'Auckland', 'Address1': '503, Avenue Park', 'LName': 'Shew', 'Phone': '19809224478', 'FName': 'Matt', 'Country': 'NZ', 'Email': 'matt#xxx.com'}
{'City': 'Austraila', 'Address1': '503, Baker Street Mickey Park Suite 510', 'LName': 'Smith', 'Phone': '19807824478', 'FName': 'Patt', 'Country': 'AZ', 'Email': 'patt#xxx.com'}
{'City': 'Chicago', 'Address1': '12, Main St. 21st Lane Suit 290', 'LName': 'Stew', 'Phone': '19809224478', 'FName': 'Doug', 'Country': 'US', 'Email': 'doug#xxx.com'}
{'City': 'NY', 'Address1': '88, Washington Park', 'LName': 'Mark', 'Phone': '19809224478', 'FName': 'Henry', 'Country': 'US', 'Email': 'matt#xxx.com'}
Any suggestions guys ???
PS:
I have more than 100K such records in my csv file !!!
You can replace the print row with a dict comprehsion that replaces newlines in the values:
row = {k: v.replace('\n', ' ') for k, v in row.iteritems()}
print row