Develop Reference

Split each line in a file based on delimitters

This is the sample data in a file. I want to split each line in the file and add to a dataframe. In some cases they have more than 1 child. So whenever they have more than one child new set of column have to be added child2 Name and DOB
(P322) Rashmika Chadda 15/05/1995 – Rashmi C 12/02/2024
(P324) Shiva Bhupati 01/01/1994 – Vinitha B 04/08/2024
(P356) Karthikeyan chandrashekar 22/02/1991 – Kanishka P 10/03/2014
(P366) Kalyani Manoj 23/01/1975 - Vandana M 15/05/1995 - Chandana M 18/11/1998
This is the code I have tried but this splits only by taking "-" into consideration
with open("text.txt") as read_file:
file_contents = read_file.readlines()
content_list = []
temp = []
for each_line in file_contents:
temp = each_line.replace("–", " ").split()
content_list.append(temp)
print(content_list)
Current output:
[['(P322)', 'Rashmika', 'Chadda', '15/05/1995', 'Rashmi', 'Chadda', 'Teega', '12/02/2024'], ['(P324)', 'Shiva', 'Bhupati', '01/01/1994', 'Vinitha', 'B', 'Sahu', '04/08/2024'], ['(P356)', 'Karthikeyan', 'chandrashekar', '22/02/1991', 'Kanishka', 'P', '10/03/2014'], ['(P366)', 'Kalyani', 'Manoj', '23/01/1975', '-', 'Vandana', 'M', '15/05/1995', '-', 'Chandana', 'M', '18/11/1998']]
Final output should be like below
Code
Parent_Name
DOB
Child1_Name
DOB
Child2_Name
DOB
P322
Rashmika Chadda
15/05/1995
Rashmi C
12/02/2024
P324
Shiva Bhupati
01/01/1994
Vinitha B
04/08/2024
P356
Karthikeyan chandrashekar
22/02/1991
Kanishka P
10/03/2014
P366
Kalyani Manoj
23/01/1975
Vandana M
15/05/1995
Chandana M
18/11/1998

I'm not sure if you want it as a list or something else.
To get lists:
result = []
for t in text[:]:
# remove the \n at the end of each line
t = t.strip()
# remove the parenthesis you don't wnt
t = t.replace("(", "")
t = t.replace(")", "")
# split on space
t = t.split(" – ")
# reconstruct
for i, person in enumerate(t):
person = person.split(" ")
# print(person)
# remove code
if i==0:
res = [person.pop(0)]
res.extend([" ".join(person[:2]), person[2]])
result.append(res)
print(result)
Which would give the below output:
[['P322', 'Rashmika Chadda', '15/05/1995', 'Rashmi C', '12/02/2024'], ['P324', 'Shiva Bhupati', '01/01/1994', 'Vinitha B', '04/08/2024'], ['P356', 'Karthikeyan chandrashekar', '22/02/1991', 'Kanishka P', '10/03/2014'], ['P366', 'Kalyani Manoj', '23/01/1975', 'Vandana M', '15/05/1995', 'Chandana M', '18/11/1998']]
You can organise a bit more the data using dictionnary:
result = {}
for t in text[:]:
# remove the \n at the end of each line
t = t.strip()
# remove the parenthesis you don't wnt
t = t.replace("(", "")
t = t.replace(")", "")
# split on space
t = t.split(" – ")
for i, person in enumerate(t):
# split name
person = person.split(" ")
# remove code
if i==0:
code = person.pop(0)
if i==0:
result[code] = {"parent_name": " ".join(person[:2]), "parent_DOB": person[2], "children": [] }
else:
result[code]['children'].append({f"child{i}_name": " ".join(person[:2]), f"child{i}_DOB": person[2]})
print(result)
Which would give this output:
{'P322': {'children': [{'child1_DOB': '12/02/2024',
'child1_name': 'Rashmi C'}],
'parent_DOB': '15/05/1995',
'parent_name': 'Rashmika Chadda'},
'P324': {'children': [{'child1_DOB': '04/08/2024',
'child1_name': 'Vinitha B'}],
'parent_DOB': '01/01/1994',
'parent_name': 'Shiva Bhupati'},
'P356': {'children': [{'child1_DOB': '10/03/2014',
'child1_name': 'Kanishka P'}],
'parent_DOB': '22/02/1991',
'parent_name': 'Karthikeyan chandrashekar'},
'P366': {'children': [{'child1_DOB': '15/05/1995',
'child1_name': 'Vandana M'},
{'child2_DOB': '18/11/1998', 'child2_name': 'Chandana M'}],
'parent_DOB': '23/01/1975',
'parent_name': 'Kalyani Manoj'}}
In the end, to have an actual table, you would need to use pandas but that will require for you to fix the number of children max so that you can pad the empty cells.

How to clean and structure a messy csv file into a pandas dataframe

I have a hard time formatting my csv files in a way easy to process in a pandas dataframe. I am using this https://figshare.com/articles/UMA_ADL_FALL_Dataset_zip/4214283 dataset of fall data to train a RNN model to detect people falling but the formatting is quite hard to clean up with the python csv reader and even with a more intelligent module clevercsv.
this is the code to itterate over the files and merge them into a Dataframe:
import pandas as pd
import zipfile
import clevercsv as csv
csv_list = []
directory = r"C:\Users\20191678\OneDrive - TU Eindhoven\Engineering Design"
for filename in os.listdir(directory):
if '.csv' in filename:
with open(filename, "r", newline="") as fp:
dialect = csv.Sniffer().sniff(fp.read(), verbose=True)
fp.seek(0)
reader = csv.reader(fp, dialect)
rows = list(reader)
csv_list.append(rows)
df = pd.DataFrame(csv_list)
Would be great if anyone can take the time to solve this and make a structured dataframe! Or come up with another idea of cleaning this up.
The csv file code itself:
% Universidad de Malaga - ETSI de Telecomunicacion (Spain)
% Date: 2017-04-14_23:38:23
% ID: Subject_01_ADL_Aplausing_1
% Name: Subject_01
% Age: 67
% Height(cm): 156
% Weight(Kg): 76
% Gender: F
% Type of Movement: ADL
% Type of Movement: FALSE
% Description of the movement: Aplausing
% Trial: 1
% Number of Sensors: 5
% Used Smartphone: LGE-lge-LG-H815-5.1
% Smartphone's Accelerometer: LGE Accelerometer - Vendor: BOSCH
% --> Version: 1
% --> Min - Max Delay: 5000us - 65535000us
% --> Maximum Range: 16.000000263891405 G
% --> Resolution: 1.2136514986004396E-4 G
% SensorTag's Accelerometer: MPU-9250 MEMS MotionTracking Device - Invensense
% --> Maximum Range: 16 G
% --> Resolution: 0.00024 G
% MAC Address; Sensor_ID; Position; Device Model
%f8:95:c7:f3:ba:82; 0; RIGHTPOCKET; lge-LG-H815-5.1
%C4:BE:84:71:A5:02; 2; WAIST; SensorTag
%C4:BE:84:70:0E:80; 3; WRIST; SensorTag
%B0:B4:48:B8:77:03; 4; ANKLE; SensorTag
%C4:BE:84:70:64:8A; 1; CHEST; SensorTag
% Sensor_Type:
% Accelerometer = 0
% Gyroscope = 1
% Magnetometer = 2
% TimeStamp; Sample No; X-Axis; Y-Axis; Z-Axis; Sensor Type; Sensor ID;
102;1;-0.1387496441602707;0.8868721723556519;0.3310287296772003;0;0
102;2;-0.1381397247314453;0.8865065574645996;0.3323715031147003;0;0
102;3;-0.1348443180322647;0.8895576596260071;0.3311501145362854;0;0
102;4;-0.1402153074741364;0.8866279125213623;0.3337142467498779;0;0
102;5;-0.1391168385744095;0.8862622380256653;0.3345684409141541;0;0
102;6;-0.138628289103508;0.8871164321899414;0.3346897959709168;0;0
102;7;-0.1367969810962677;0.8880935311317444;0.3412821888923645;0;0
102;8;-0.138628289103508;0.8883378505706787;0.3398165106773377;0;0
102;9;-0.1409481465816498;0.8901675939559937;0.3401837050914764;0;0
102;10;-0.1418023407459259;0.8891920447349548;0.3418920934200287;0;0
102;11;-0.1430221647024155;0.8882149457931519;0.3420134484767914;0;0
103;12;-0.143510714173317;0.8880935311317444;0.3422577381134033;0;0
103;13;-0.1439992785453796;0.8838210105895996;0.3379867672920227;0;0
103;14;-0.1431450843811035;0.8795484900474548;0.3353012502193451;0;0
103;15;-0.1438763588666916;0.8766187429428101;0.3331027626991272;0;0
103;16;-0.1429008096456528;0.8790599703788757;0.3321272134780884;0;0
103;17;-0.142656534910202;0.8779615163803101;0.3343241512775421;0;0
103;18;-0.1409481465816498;0.8801584243774414;0.3348127007484436;0;0
103;19;-0.1429008096456528;0.8816241025924683;0.3376195728778839;0;0
103;20;-0.1457076668739319;0.8821110725402832;0.3385966718196869;0;0
109;21;-0.1441206336021423;0.8832111358642578;0.3412821888923645;0;0
115;22;-0.1387496441602707;0.8832111358642578;0.3404279947280884;0;0
115;23;-0.1391168385744095;0.8822340369224548;0.3404279947280884;0;0
121;24;-0.1375298053026199;0.8843095898628235;0.3399394154548645;0;0
126;25;-0.1369199007749558;0.8868721723556519;0.337375283241272;0;0
133;26;-0.1375298053026199;0.8854080438613892;0.331394374370575;0;0

Something like this should get you going.
from pprint import pprint
def try_number(s):
try:
if "." in s:
return float(s)
return int(s, 10)
except ValueError:
return s
def read_umafall(fp):
header_lines = []
metadata = {}
data = []
for line in fp:
line = line.strip()
if line.startswith("%"):
if ": " in line:
key, _, value = line[1:].partition(": ")
metadata[key.strip()] = value
else:
header_lines.append(line)
elif ";" in line:
data.append([try_number(c) for c in line.split(";")])
elif line:
print("???", line)
return {
"header_lines": header_lines,
"metadata": metadata,
"data": data,
}
with open(
"UMAFall_Subject_01_ADL_HandsUp_2_2017-04-14_23-33-21.csv",
"r",
) as fp:
result = read_umafall(fp)
pprint(result["metadata"])
pprint(result["header_lines"])
pprint(result["data"][:10])
The output is e.g.
{'--> Maximum Range': '16 G',
'--> Min - Max Delay': '5000us - 65535000us',
'--> Resolution': '0.00024 G',
'--> Version': '1',
'Age': '67',
'Date': '2017-04-14_23:33:21',
'Description of the movement': 'HandsUp',
'Gender': 'F',
'Height(cm)': '156',
'ID': 'Subject_01_ADL_HandsUp_2',
'Name': 'Subject_01',
'Number of Sensors': '5',
"SensorTag's Accelerometer": 'MPU-9250 MEMS MotionTracking Device - '
'Invensense',
"Smartphone's Accelerometer": 'LGE Accelerometer - Vendor: BOSCH',
'Trial': '2',
'Type of Movement': 'FALSE',
'Used Smartphone': 'LGE-lge-LG-H815-5.1',
'Weight(Kg)': '76'}
['% Universidad de Malaga - ETSI de Telecomunicacion (Spain)',
'% MAC Address; Sensor_ID; Position; Device Model',
'%f8:95:c7:f3:ba:82; 0; RIGHTPOCKET; lge-LG-H815-5.1',
'%C4:BE:84:71:A5:02; 2; WAIST; SensorTag',
'%C4:BE:84:70:0E:80; 3; WRIST; SensorTag',
'%B0:B4:48:B8:77:03; 4; ANKLE; SensorTag',
'%C4:BE:84:70:64:8A; 1; CHEST; SensorTag',
'% Sensor_Type:',
'% Accelerometer = 0',
'% Gyroscope = 1',
'% Magnetometer = 2',
'% TimeStamp; Sample No; X-Axis; Y-Axis; Z-Axis; Sensor Type; Sensor ID;']
[[371, 1, -0.01265575457364321, 0.9133599400520325, -0.1938552260398865, 0, 0],
[371, 2, -0.01839394308626652, 0.9126286506652832, -0.1926354020833969, 0, 0],
[371, 3, -0.01802674867212772, 0.9129943251609802, -0.1948323398828507, 0, 0],
[371, 4, -0.02352065965533257, 0.9167782664299011, -0.1969063729047775, 0, 0],
[371, 5, -0.02315346524119377, 0.9209294319152832, -0.2019117176532745, 0, 0],
[371, 6, -0.01888094283640385, 0.9211721420288086, -0.203375831246376, 0, 0],
[371, 7, -0.0208351630717516, 0.9270316958427429, -0.2050857692956924, 0, 0],
[371, 8, -0.01924813725054264, 0.9303271174430847, -0.2070384472608566, 0, 0],
[371, 9, -0.01766111142933369, 0.9342340230941772, -0.2080155462026596, 0, 0],
[371, 10, -0.01265575457364321, 0.9388721585273743, -0.2115552425384522, 0, 0]]
```,
that is
* first the header lines that could be parsed as key-value pairs
* other header lines
* the data
You can hopefully trust each file to have the data in the same order (`TimeStamp; Sample No; X-Axis; Y-Axis; Z-Axis; Sensor Type; Sensor ID`).

Python how convert single quotes to double quotes to format as json string

I have a file where on each line I have text like this (representing cast of a film):
[{'cast_id': 23, 'character': "Roger 'Verbal' Kint", 'credit_id': '52fe4260c3a36847f8019af7', 'gender': 2, 'id': 1979, 'name': 'Kevin Spacey', 'order': 5, 'profile_path': '/x7wF050iuCASefLLG75s2uDPFUu.jpg'}, {'cast_id': 27, 'character': 'Edie's Finneran', 'credit_id': '52fe4260c3a36847f8019b07', 'gender': 1, 'id': 2179, 'name': 'Suzy Amis', 'order': 6, 'profile_path': '/b1pjkncyLuBtMUmqD1MztD2SG80.jpg'}]
I need to convert it in a valid json string, thus converting only the necessary single quotes to double quotes (e.g. the single quotes around word Verbal must not be converted, eventual apostrophes in the text also should not be converted).
I am using python 3.x. I need to find a regular expression which will convert only the right single quotes to double quotes, thus the whole text resulting in a valid json string. Any idea?

First of all, the line you gave as example is not parsable! … 'Edie's Finneran' … contains a syntax error, not matter what.
Assuming that you have control over the input, you could simply use eval() to read in the file. (Although, in that case one would wonder why you can't produce valid JSON in the first place…)
>>> f = open('list.txt', 'r')
>>> s = f.read().strip()
>>> l = eval(s)
>>> import pprint
>>> pprint.pprint(l)
[{'cast_id': 23,
'character': "Roger 'Verbal' Kint",
...
'profile_path': '/b1pjkncyLuBtMUmqD1MztD2SG80.jpg'}]
>>> import json
>>> json.dumps(l)
'[{"cast_id": 23, "character": "Roger \'Verbal\' Kint", "credit_id": "52fe4260ca36847f8019af7", "gender": 2, "id": 1979, "name": "Kevin Spacey", "order": 5, "rofile_path": "/x7wF050iuCASefLLG75s2uDPFUu.jpg"}, {"cast_id": 27, "character":"Edie\'s Finneran", "credit_id": "52fe4260c3a36847f8019b07", "gender": 1, "id":2179, "name": "Suzy Amis", "order": 6, "profile_path": "/b1pjkncyLuBtMUmqD1MztDSG80.jpg"}]'
If you don't have control over the input, this is very dangerous, as it opens you up to code injection attacks.
I cannot emphasize enough that the best solution would be to produce valid JSON in the first place.

If you do not have control over the JSON data, do not eval() it!
I created a simple JSON correction mechanism, as that is more secure:
def correctSingleQuoteJSON(s):
rstr = ""
escaped = False
for c in s:
if c == "'" and not escaped:
c = '"' # replace single with double quote
elif c == "'" and escaped:
rstr = rstr[:-1] # remove escape character before single quotes
elif c == '"':
c = '\\' + c # escape existing double quotes
escaped = (c == "\\") # check for an escape character
rstr += c # append the correct json
return rstr
You can use the function in the following way:
import json
singleQuoteJson = "[{'cast_id': 23, 'character': 'Roger \\'Verbal\\' Kint', 'credit_id': '52fe4260c3a36847f8019af7', 'gender': 2, 'id': 1979, 'name': 'Kevin Spacey', 'order': 5, 'profile_path': '/x7wF050iuCASefLLG75s2uDPFUu.jpg'}, {'cast_id': 27, 'character': 'Edie\\'s Finneran', 'credit_id': '52fe4260c3a36847f8019b07', 'gender': 1, 'id': 2179, 'name': 'Suzy Amis', 'order': 6, 'profile_path': '/b1pjkncyLuBtMUmqD1MztD2SG80.jpg'}]"
correctJson = correctSingleQuoteJSON(singleQuoteJson)
print(json.loads(correctJson))

Here is the code to get desired output
import ast
def getJson(filepath):
fr = open(filepath, 'r')
lines = []
for line in fr.readlines():
line_split = line.split(",")
set_line_split = []
for i in line_split:
i_split = i.split(":")
i_set_split = []
for split_i in i_split:
set_split_i = ""
rev = ""
i = 0
for ch in split_i:
if ch in ['\"','\'']:
set_split_i += ch
i += 1
break
else:
set_split_i += ch
i += 1
i_rev = (split_i[i:])[::-1]
state = False
for ch in i_rev:
if ch in ['\"','\''] and state == False:
rev += ch
state = True
elif ch in ['\"','\''] and state == True:
rev += ch+"\\"
else:
rev += ch
i_rev = rev[::-1]
set_split_i += i_rev
i_set_split.append(set_split_i)
set_line_split.append(":".join(i_set_split))
line_modified = ",".join(set_line_split)
lines.append(ast.literal_eval(str(line_modified)))
return lines
lines = getJson('test.txt')
for i in lines:
print(i)

Apart from eval() (mentioned in user3850's answer), you can use ast.literal_eval
This has been discussed in the thread: Using python's eval() vs. ast.literal_eval()?
You can also look at the following discussion threads from Kaggle competition which has data similar to the one mentioned by OP:
https://www.kaggle.com/c/tmdb-box-office-prediction/discussion/89313#latest-517927
https://www.kaggle.com/c/tmdb-box-office-prediction/discussion/80045#latest-518338

Odd Behaviour of Loop in Python

I am writing a script to report statistics from a text file in Markdown. The file contains book titles and dates. Each date belongs to the titles that follow, until a new date appears. Here is a sample:
#### 8/23/05
Defining the World (Hitchings)
#### 8/26/05
Lost Japan
#### 9/5/05
The Kite Runner
*The Dark Valley (Brendon)*
#### 9/9/05
Active Liberty
I iterate over lines in the file with a for loop and examine each line to see if it's a date. If it's a date, I set a variable this_date. If it's a title, I make it into a dict with the current value of this_date.
There are two exceptions: the file starts with titles, not a date, so I set an initial value for this_date before the for loop. And halfway through the file there is a region where dates were lost, and I set a specific date for those titles.
But in the resulting list of dicts, all the titles are given that date until the lost-data region starts. After that point, the rest of the titles are given the date that appears last in the file. What is most confusing: when I print the contents of this_date right before appending the new dict, it contains the correct value on every loop.
I expect this_date to be visible at all levels of the loop. I know I need to break this up into functions, and passing results explicitly between functions will probably fix the issue, but I'd like to know why this approach didn't work. Thank you very much.
result = []
# regex patterns
ddp = re.compile('\d+') # extract digits
mp = re.compile('^#+\s*\d+') # captures hashes and spaces
dp = re.compile('/\d+/') # captures slashes
yp = re.compile('\d+$')
sp = re.compile('^\*')
# initialize
this_date = {
'month': 4,
'day': 30,
'year': 2005
}
# print('this_date initialized')
for line in text:
if line == '':
pass
else:
if '#' in line: # markdown header format - line is a new date
if 'Reconstructing lost data' in line: # handle exception
# titles after this line are given 12/31/14 (the last date in the file) instead of 8/31/10
# all prior dates are overwritten with 8/31/10
# but the intent is that titles after this line appears have date 8/31/10, until the next date
this_date = {
'month': 8,
'day': 31,
'year': 2010
}
# print('set this_date to handle exception')
else: # get the date from the header
month = ddp.search( mp.search(line).group() ) # digits only
day = ddp.search( dp.search(line).group() ) # digits only
year = yp.search(line)
if month and day and year:
# print('setting this_date within header parse')
this_date['month'] = int(month.group())
this_date['day'] = int(day.group())
this_date['year'] = ( int(year.group()) + 2000 )
else:
pass
else: # line is a title
x = {
'date': this_date,
'read': False
}
if sp.match(line): # starts with asterisk - has been read
x['read'] = True
x['title'] = line[1:-3] # trim trailing asterisk and spaces
else:
x['title'] = line
# this_date is correct when printed here
# print('this_date is ' + str(this_date['month']) + '/' + str(this_date['day']) + '/' + str(this_date['year']) )
result.append(x)
# x has correct date when printed here
# print(x)
# print("Done; found %d titles.") % len(result)
# elements of result have wrong dates (either 8/31/10 or 12/31/14, no other values) when printed here
# print( result[0::20])

You create the this_date dictionary just once. You then reuse that dictionary each loop iteration. You are only adding references to that dictionary to your result list; it is just the one dictionary referenced over and over again.
Store a new copy of the dictionary each loop iteration:
x = {
'date': this_date.copy(),
'read': False
}
Your code could do with some simplification; I'd use datetime.date() objects here instead as they model dates properly. No regular expressions are required:
from datetime import datetime
current_date = None
results = []
for line in text:
line = line.strip()
if not line:
continue
if line.startswith('#'):
current_date = datetime.strptime(line.strip('# '), '%m/%d/%y').date()
continue
entry = {'date': current_date, 'read': False}
if line.startswith('*') and line.endswith('*'):
# previously read
line = line.strip('*')
entry['read'] = True
entry['title'] = line
results.append(entry)
Because datetime.date() objects are immutable and we create a new date object each time we encounter a header line, you can safely re-use the last-read date.
Demo:
>>> from datetime import datetime
>>> from pprint import pprint
>>> text = '''\
... #### 8/23/05
... Defining the World (Hitchings)
... #### 8/26/05
... Lost Japan
... #### 9/5/05
... The Kite Runner
... *The Dark Valley (Brendon)*
... #### 9/9/05
... Active Liberty
... '''.splitlines(True)
>>> current_date = None
>>> results = []
>>> for line in text:
... line = line.strip()
... if not line:
... continue
... if line.startswith('#'):
... current_date = datetime.strptime(line.strip('# '), '%m/%d/%y').date()
... continue
... entry = {'date': current_date, 'read': False}
... if line.startswith('*') and line.endswith('*'):
... # previously read
... line = line.strip('*')
... entry['read'] = True
... entry['title'] = line
... results.append(entry)
...
>>> pprint(results)
[{'date': datetime.date(2005, 8, 23),
'read': False,
'title': 'Defining the World (Hitchings)'},
{'date': datetime.date(2005, 8, 26), 'read': False, 'title': 'Lost Japan'},
{'date': datetime.date(2005, 9, 5),
'read': False,
'title': 'The Kite Runner'},
{'date': datetime.date(2005, 9, 5),
'read': True,
'title': 'The Dark Valley (Brendon)'},
{'date': datetime.date(2005, 9, 9), 'read': False, 'title': 'Active Liberty'}]

Write a List Into Excel File with Python

I have a list to be exported to an Excel file keeping the appropriate format, I resorted to a library named xlsxwriter,
here is an example :
xlsxwriter
and here is my list :
{'FirstName': u'Forence','LastName': u'Bidorst', 'Salary': -6775000.0, 'BirthDate': datetime.datetime(2013, 6, 20, 0, 0)}
{'FirstName': u'Oliver','LastName': u'Bidorst', 'Salary': -6775000.0, 'BirthDate': datetime.datetime(2013, 6, 20, 0, 0)}
{'FirstName': u'Mathew','LastName': u'Stark', 'Salary': -6775000.0, 'BirthDate': datetime.datetime(2013, 6, 20, 0, 0)}
{'FirstName': u'Sphed','LastName': u'liomst', 'Salary': -6775000.0, 'BirthDate': datetime.datetime(2013, 6, 20, 0, 0)}
I modified the code to browse a list and insert it into the file,
def export_result_XL():
list=get_list()
...
# Write some data headers.
worksheet.write('A1', 'First Name', bold)
worksheet.write('B1', 'Last Name', bold)
worksheet.write('C1', 'Salary', bold)
worksheet.write('D1', 'Birth Date', bold)
# Some data we want to write to the worksheet.
for entry in list:
x = str(entry['FirstName'])
y = str(entry['LastName'])
z = str(entry['Salary'])
e = str(entry['BirthDate'])
v = BirthDate[:10] # because the date format is like yyyy-mm-dd 00:00:00
expenses = (
[x,y ,z ,v]
)
# Start from the first cell below the headers.
row = 1
col = 0
for item ,str1,str2,str3,date_str in (expenses):
# Convert the date string into a datetime object.
date = datetime.strptime(date_str, "%Y-%m-%d")
worksheet.write_string (row, col, str1 )
worksheet.write_string (row, col + 1, str2 )
worksheet.write_string(row, col + 2, str3 )
worksheet.write_datetime(row, col + 3, date, date_format )
row += 1
# Write a total using a formula.
#worksheet.write(row, 0, 'Total', bold)
#worksheet.write(row, 2, '=SUM(C2:C5)', money_format)
workbook.close()
return ''
I had two problems here :
1 -
for item, date_str in (frais)
ValueError: too many values ​​to unpack
2-
if I avoid to convert to date format the file will be genreated but columns and rows are flipped
Is there any Idea how to do it , I hope I was clear in the description

Finally I found a Solution :
row = 1
col = 0
for entry in list:
print entry
strdate=str(entry['BirthDate'])
formatdate=strdate[:10]
date = datetime.strptime(str(formatdate), "%Y-%m-%d")
worksheet.write_string (row, col, entry['FirstName'] )
worksheet.write_string (row, col+1, entry['LastName'] )
worksheet.write_number (row, col+6, entry['Salary'],number_format )
worksheet.write_datetime(row, col+10, date, date_format )
row += 1
workbook.close()