Module 3 - Day 4

Working with databases

There s different connector for connecting with diffrent databases.
once the connection is established syntax is same.
For training we will use sqlite3

connecting and creating

import sqlite3

conn = sqlite3.connect("training.db") # if the database does not exist, it will create
cur = conn.cursor()
cur.execute("create table person (name varchar(100), email varchar(100));")
cur.execute("insert into person (name, email) values ('alice', 'alice@wonder.land');")
conn.commit()

conn.close()

Access data

conn = sqlite3.connect("training.db")

cur = conn.cursor()
results = cur.execute("select * from person")

results # some kind of iterator!

<sqlite3.Cursor at 0x7f2f6da5ba40>

results.fetchall()

[('alice', 'alice@wonder.land')]

def find_person(conn, email):
    q = f"select * from person where email='{email}'" # not a good way!
    print(q)
    cur = conn.cursor()
    r = cur.execute(q)
    return r.fetchall()

find_person(conn, "alice@wonder.land")

select * from person where email='alice@wonder.land'

[('alice', 'alice@wonder.land')]

find_person(conn, "alex@newyork.zoo")

select * from person where email='alex@newyork.zoo'

[]

def find_person_betterway(conn, email):
    q = "select * from person where email=?"
    cur = conn.cursor()
    r = cur.execute(q, (email,))
    return r.fetchall()

find_person_betterway(conn, "alice@wonder.land")

[('alice', 'alice@wonder.land')]

[1, 2, 3, 4]

[1, 2, 3, 4]

(2, 3, 4, 5)

(2, 3, 4, 5)

[1]

[1]

(1) # this does not from a tuple!

(2+3)

(3,) # this makes a tuple with single value

(3,)

lets make a generic query

def query(conn, querystring, params=None):
    cur = conn.cursor()
    r = cur.execute(querystring, params)
    return r.fetchall()

query("select * from person where name=?", ("alice",))

AttributeError: 'str' object has no attribute 'cursor'

query(conn, "select * from person where name=?", ("alice",))

[('alice', 'alice@wonder.land')]

# complete these function which will make use of above query function
def find_person_with_name(conn, name):
    return query("select * from person where name=?", (name,))

def find_person_with_email(conn, email):
    return query("select * from person where email=?",(email,))

find_person_with_email(conn, "alice@wonder.land")

AttributeError: 'str' object has no attribute 'cursor'

# complete these function which will make use of above query function
def find_person_with_name(conn, name):
    return query(conn, "select * from person where name=?", (name,))

def find_person_with_email(conn, email):
    return query(conn, "select * from person where email=?",(email,))

find_person_with_name(conn, "alice")

[('alice', 'alice@wonder.land')]

find_person_with_email(conn, "alice@wonder.land")

[('alice', 'alice@wonder.land')]

query(conn, "select * from person where name=?")

ValueError: parameters are of unsupported type

def query(conn, querystring, params=None):
    if params is None:
        params = tuple()
    cur = conn.cursor()
    r = cur.execute(querystring, params)
    return r.fetchall()

query(conn, "select * from person")

[('alice', 'alice@wonder.land')]

query(conn, "insert into person (name, email) values (?,?)", ("alex","alex@newyork.zoo"))

[]

query(conn, "select * from person")

[('alice', 'alice@wonder.land'), ('alex', 'alex@newyork.zoo')]

pandas and databases

import pandas as pd

wallet = pd.read_csv("wallet.csv")

wallet

	Unnamed: 0	date	category	description	debit
0	0	2021-03-07 14:53:28.377359	Music	Amazon	421.207327
1	1	2020-10-08 09:53:28.377359	Food	Swiggy	328.440080
2	2	2021-02-23 09:53:28.377359	Books	Amazon	244.679437
3	3	2020-11-01 14:53:28.377359	Utility	Phone	222.756318
4	4	2021-06-05 13:53:28.377359	Books	Flipcart	494.128492
...	...	...	...	...	...
95	95	2021-07-19 13:53:28.377359	Utility	Phone	388.671213
96	96	2021-01-12 19:53:28.377359	Books	Flipcart	467.554562
97	97	2021-03-25 11:53:28.377359	Utility	Phone	320.789434
98	98	2021-05-13 15:53:28.377359	Travel	Taxi	442.096469
99	99	2020-10-11 16:53:28.377359	Food	Hotel	100.455501

100 rows × 5 columns

del wallet['Unnamed: 0']

wallet

	date	category	description	debit
0	2021-03-07 14:53:28.377359	Music	Amazon	421.207327
1	2020-10-08 09:53:28.377359	Food	Swiggy	328.440080
2	2021-02-23 09:53:28.377359	Books	Amazon	244.679437
3	2020-11-01 14:53:28.377359	Utility	Phone	222.756318
4	2021-06-05 13:53:28.377359	Books	Flipcart	494.128492
...	...	...	...	...
95	2021-07-19 13:53:28.377359	Utility	Phone	388.671213
96	2021-01-12 19:53:28.377359	Books	Flipcart	467.554562
97	2021-03-25 11:53:28.377359	Utility	Phone	320.789434
98	2021-05-13 15:53:28.377359	Travel	Taxi	442.096469
99	2020-10-11 16:53:28.377359	Food	Hotel	100.455501

100 rows × 4 columns

wallet.to_sql("expenses", con=conn, if_exists="replace") # before your program does any kind of write/alteration to data in database, test it out well in test database

r = conn.cursor().execute("select * from expenses")

for item in r:
    print(item)

(0, '2021-03-07 14:53:28.377359', 'Music', 'Amazon', 421.2073272347991)
(1, '2020-10-08 09:53:28.377359', 'Food', 'Swiggy', 328.4400802428426)
(2, '2021-02-23 09:53:28.377359', 'Books', 'Amazon', 244.67943701511356)
(3, '2020-11-01 14:53:28.377359', 'Utility', 'Phone', 222.7563175805277)
(4, '2021-06-05 13:53:28.377359', 'Books', 'Flipcart', 494.1284923793595)
(5, '2021-07-28 19:53:28.377359', 'Utility', 'Electricity', 219.94171130968408)
(6, '2021-04-16 11:53:28.377359', 'Books', 'Amazon Kindle', 270.32259514795845)
(7, '2021-02-15 10:53:28.377359', 'Food', 'Zomato', 457.1831036346536)
(8, '2021-08-10 19:53:28.377359', 'Utility', 'Phone', 151.49637259947792)
(9, '2020-11-29 14:53:28.377359', 'Travel', 'Auto', 443.61888423247854)
(10, '2021-06-15 13:53:28.377359', 'Travel', 'Metro', 328.1754210974373)
(11, '2021-07-24 13:53:28.377359', 'Food', 'Zomato', 434.4954675355444)
(12, '2021-07-24 14:53:28.377359', 'Music', 'Amazon', 329.5360031897569)
(13, '2021-06-06 10:53:28.377359', 'Utility', 'Phone', 154.0449491816659)
(14, '2021-06-09 13:53:28.377359', 'Travel', 'Taxi', 485.2977429821982)
(15, '2021-08-24 17:53:28.377359', 'Food', 'Zomato', 262.9439932340398)
(16, '2021-03-05 19:53:28.377359', 'Utility', 'Phone', 390.31687619327926)
(17, '2021-04-17 18:53:28.377359', 'Utility', 'Electricity', 316.8786754246636)
(18, '2021-05-08 15:53:28.377359', 'Travel', 'Auto', 433.8224042777936)
(19, '2021-05-16 10:53:28.377359', 'Books', 'Flipcart', 109.32590886550068)
(20, '2020-10-12 18:53:28.377359', 'Travel', 'Auto', 365.9218082537661)
(21, '2021-01-04 19:53:28.377359', 'Travel', 'Metro', 329.09737150258513)
(22, '2021-06-24 15:53:28.377359', 'Food', 'Zomato', 489.1434830522253)
(23, '2020-12-11 10:53:28.377359', 'Music', 'Netflix', 354.9402409919816)
(24, '2021-05-31 11:53:28.377359', 'Books', 'Amazon', 498.10049550461065)
(25, '2021-05-21 14:53:28.377359', 'Food', 'Hotel', 483.315863517772)
(26, '2020-08-26 15:53:28.377359', 'Books', 'Amazon Kindle', 138.806577801854)
(27, '2021-05-01 15:53:28.377359', 'Utility', 'Electricity', 103.68079074846584)
(28, '2020-12-14 15:53:28.377359', 'Utility', 'Phone', 358.4599327957656)
(29, '2021-06-20 10:53:28.377359', 'Utility', 'Electricity', 184.5577284049955)
(30, '2020-09-15 18:53:28.377359', 'Food', 'Swiggy', 203.5292397894327)
(31, '2020-09-25 11:53:28.377359', 'Books', 'Flipcart', 246.503527384528)
(32, '2021-06-23 11:53:28.377359', 'Food', 'Zomato', 345.03043608141513)
(33, '2021-05-14 18:53:28.377359', 'Food', 'Hotel', 449.24802955761743)
(34, '2021-05-14 10:53:28.377359', 'Utility', 'Phone', 499.8581815222449)
(35, '2021-02-18 18:53:28.377359', 'Travel', 'Metro', 441.6021430011205)
(36, '2020-12-10 10:53:28.377359', 'Travel', 'Auto', 472.94143917262176)
(37, '2021-04-18 16:53:28.377359', 'Music', 'Amazon', 266.0690783774673)
(38, '2021-08-15 10:53:28.377359', 'Travel', 'Auto', 494.1243994056571)
(39, '2021-05-17 17:53:28.377359', 'Food', 'Swiggy', 112.33316019807457)
(40, '2021-07-19 12:53:28.377359', 'Food', 'Swiggy', 291.54598801930536)
(41, '2021-02-20 19:53:28.377359', 'Utility', 'Phone', 425.18719068071806)
(42, '2021-08-22 17:53:28.377359', 'Food', 'Hotel', 210.2562695007857)
(43, '2020-09-21 12:53:28.377359', 'Utility', 'Phone', 486.0339327616073)
(44, '2020-12-26 19:53:28.377359', 'Utility', 'Electricity', 257.92759337085425)
(45, '2021-05-27 16:53:28.377359', 'Utility', 'Electricity', 154.74287259516657)
(46, '2021-05-15 15:53:28.377359', 'Utility', 'Electricity', 359.3249716537848)
(47, '2020-10-28 10:53:28.377359', 'Books', 'Flipcart', 310.408610004679)
(48, '2021-08-23 17:53:28.377359', 'Utility', 'Electricity', 310.05840961423314)
(49, '2021-03-16 09:53:28.377359', 'Music', 'spotify', 232.30340219121135)
(50, '2020-12-24 11:53:28.377359', 'Food', 'Zomato', 463.0018749263554)
(51, '2020-12-22 17:53:28.377359', 'Food', 'Zomato', 331.22702332837093)
(52, '2021-03-26 09:53:28.377359', 'Travel', 'Taxi', 403.6100701341934)
(53, '2021-01-27 09:53:28.377359', 'Utility', 'Electricity', 183.1866624101276)
(54, '2020-11-16 10:53:28.377359', 'Music', 'spotify', 160.81754340768396)
(55, '2021-01-21 19:53:28.377359', 'Books', 'Flipcart', 423.74970808720553)
(56, '2021-05-19 18:53:28.377359', 'Utility', 'Phone', 319.3428762684619)
(57, '2021-07-15 15:53:28.377359', 'Utility', 'Phone', 279.6090437716363)
(58, '2021-05-20 10:53:28.377359', 'Food', 'Hotel', 255.8710346734312)
(59, '2020-08-28 11:53:28.377359', 'Food', 'Swiggy', 208.2329120852039)
(60, '2021-01-17 11:53:28.377359', 'Utility', 'Electricity', 382.5195101154448)
(61, '2021-02-25 13:53:28.377359', 'Food', 'Hotel', 124.65827844174062)
(62, '2021-01-27 19:53:28.377359', 'Books', 'Amazon Kindle', 497.7708601564023)
(63, '2021-05-10 11:53:28.377359', 'Travel', 'Taxi', 355.9890502253258)
(64, '2021-01-31 14:53:28.377359', 'Food', 'Zomato', 232.2223798622789)
(65, '2020-10-23 18:53:28.377359', 'Music', 'Netflix', 188.7487426895118)
(66, '2020-10-09 16:53:28.377359', 'Food', 'Swiggy', 263.9577700340145)
(67, '2021-07-31 14:53:28.377359', 'Music', 'Netflix', 324.786916846731)
(68, '2020-08-26 09:53:28.377359', 'Travel', 'Taxi', 279.1478844739421)
(69, '2020-10-10 15:53:28.377359', 'Utility', 'Electricity', 300.52462041935115)
(70, '2021-08-17 13:53:28.377359', 'Utility', 'Phone', 125.22977317126336)
(71, '2021-03-30 12:53:28.377359', 'Food', 'Swiggy', 245.36050838040904)
(72, '2021-06-30 18:53:28.377359', 'Books', 'Amazon', 294.6628689900488)
(73, '2021-08-15 17:53:28.377359', 'Travel', 'Metro', 117.58872931045572)
(74, '2021-03-20 11:53:28.377359', 'Travel', 'Taxi', 303.05542098520453)
(75, '2021-03-03 12:53:28.377359', 'Food', 'Hotel', 425.6252909948148)
(76, '2020-11-17 09:53:28.377359', 'Music', 'Netflix', 197.5346000167895)
(77, '2021-01-18 14:53:28.377359', 'Books', 'Amazon Kindle', 482.1523430204321)
(78, '2020-09-09 16:53:28.377359', 'Music', 'spotify', 415.3728938035302)
(79, '2021-08-17 09:53:28.377359', 'Music', 'Netflix', 321.7634156544651)
(80, '2021-02-17 09:53:28.377359', 'Food', 'Swiggy', 283.09570727160764)
(81, '2020-10-29 16:53:28.377359', 'Food', 'Hotel', 470.08099539923614)
(82, '2020-09-22 09:53:28.377359', 'Music', 'spotify', 411.14270120842224)
(83, '2021-03-18 09:53:28.377359', 'Books', 'Flipcart', 451.5844070294999)
(84, '2020-09-21 10:53:28.377359', 'Music', 'Netflix', 158.7936457269333)
(85, '2021-01-12 09:53:28.377359', 'Music', 'Amazon', 130.37490757527)
(86, '2021-05-07 16:53:28.377359', 'Food', 'Zomato', 198.450671792638)
(87, '2021-05-19 15:53:28.377359', 'Food', 'Zomato', 378.82064134052473)
(88, '2021-04-18 09:53:28.377359', 'Utility', 'Phone', 124.2212478444578)
(89, '2021-04-12 14:53:28.377359', 'Music', 'Amazon', 218.487173429263)
(90, '2020-12-01 14:53:28.377359', 'Music', 'Amazon', 101.57327588889416)
(91, '2021-01-22 17:53:28.377359', 'Food', 'Hotel', 232.66346838787223)
(92, '2021-01-12 19:53:28.377359', 'Travel', 'Taxi', 356.8426379886326)
(93, '2021-01-11 09:53:28.377359', 'Utility', 'Electricity', 111.72080867898062)
(94, '2021-01-04 13:53:28.377359', 'Utility', 'Phone', 431.1855366816298)
(95, '2021-07-19 13:53:28.377359', 'Utility', 'Phone', 388.6712132388421)
(96, '2021-01-12 19:53:28.377359', 'Books', 'Flipcart', 467.5545618966052)
(97, '2021-03-25 11:53:28.377359', 'Utility', 'Phone', 320.78943360123816)
(98, '2021-05-13 15:53:28.377359', 'Travel', 'Taxi', 442.0964693975505)
(99, '2020-10-11 16:53:28.377359', 'Food', 'Hotel', 100.45550129902664)

def select_query(conn, tablename, *selection, **conditions):
    pass

def add(a, b):
    return a + b

add(2, 4)

add( 3, 4, 5)

TypeError: add() takes 2 positional arguments but 3 were given

def add_(*args): # variable number of unnamed arguments!
    s = 0 
    for a in args:
        s += a
    return s

add_(1, 2, 3, 4, 5, 6, 6, 6, 6)

add_(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

def person(**kwargs):# this is variable named arguments!
    print(kwargs) #this comes as dictionary

person(name="alice")

{'name': 'alice'}

person(name="alex", email="alex@zoo.com")

{'name': 'alex', 'email': 'alex@zoo.com'}

def foo(fixed_arg1, fixed_arg2, *args, x=0, y =2, **kwargs): # most generic format of arguments
    print(args)
    print(kwargs)

foo(1, 2, "a", "b", "c", "d", x=20, y=30, m=2, n=4, l=6)

('a', 'b', 'c', 'd')
{'m': 2, 'n': 4, 'l': 6}

def select_query(conn, tablename, *selection, **conditions):
    cur = conn.cursor()
    SELECTION = ",".join(selection)
    CONDITIONS = " & ".join([f"{name} = ?" for name in conditions.keys()])
    params = tuple([conditions[name] for name in conditions.keys()])
    q = f"select {SELECTION} from {tablename} where {CONDITIONS}"
    print(q)
    print(params)
    return cur.execute(q, params)

r = select_query(conn, "wallet", "description", "debit", category="Music")

select description,debit from wallet where category = ?
('Music',)

OperationalError: no such table: wallet

r = select_query(conn, "expenses", "description", "debit", category="Music")

select description,debit from expenses where category = ?
('Music',)

for items in r:
    print(items)

('Amazon', 421.2073272347991)
('Amazon', 329.5360031897569)
('Netflix', 354.9402409919816)
('Amazon', 266.0690783774673)
('spotify', 232.30340219121135)
('spotify', 160.81754340768396)
('Netflix', 188.7487426895118)
('Netflix', 324.786916846731)
('Netflix', 197.5346000167895)
('spotify', 415.3728938035302)
('Netflix', 321.7634156544651)
('spotify', 411.14270120842224)
('Netflix', 158.7936457269333)
('Amazon', 130.37490757527)
('Amazon', 218.487173429263)
('Amazon', 101.57327588889416)

def operation(*nums, op='+'):
    if op == "+":
        sum(nums)

def operation(op, *nums): # op is called as positional argument!
    if op == "+":
        sum(nums)

microsoft sql server

you will need pyodbc package for this.

install pyodbc in your virtulenv

import pyodbc
cnxn = pyodbc.connect(r'Driver={SQL Server};Server=.\SQLEXPRESS;Database=myDB;Trusted_Connection=yes;')
cursor = cnxn.cursor()
cursor.execute("SELECT LastName FROM myContacts")
while 1:
    row = cursor.fetchone()
    if not row:
        break
    print(row.LastName)
cnxn.close()

ModuleNotFoundError: No module named 'pyodbc'

regular expressions

You want to search some text in text file/ text. You can make use basic str methods for basic search.

but for complicated patterns you will have to pattern search. it can be done using regular expression

import re

empty_pattern = re.compile("^$") # this pattern says that match all empty lines ^-> start of line/text , $-> end if line/text
lines_with_single_char = re.compile("^.$") # . stands for any char!
date_like_paatern = re.compile("^\d\d\d\d-\d{1,2}-\d{1,2}") # \d stands for digit and {x,y} stands for repetion x to y times

lines = """line1
1
2
3
lkjds fd askhdkjsh

2024-3-14
2024-03-14
sdd


dfdsf"""

def matchpattern(lines, p):
    for line in lines.split("\n"):
        if p.match(line):
            print("Found a pattern!")
            print(line)

matchpattern(lines, empty_pattern)

Found a pattern!

Found a pattern!

Found a pattern!

matchpattern(lines, lines_with_single_char)

Found a pattern!
1
Found a pattern!
2
Found a pattern!
3

matchpattern(lines, date_like_paatern)

Found a pattern!
2024-3-14
Found a pattern!
2024-03-14

problem

From csv file “wallet.csv” find out roww which have timestamp between 14:00 to 15:00

!cat wallet.csv

,date,category,description,debit
0,2021-03-07 14:53:28.377359,Music,Amazon,421.2073272347991
1,2020-10-08 09:53:28.377359,Food,Swiggy,328.4400802428426
2,2021-02-23 09:53:28.377359,Books,Amazon,244.67943701511354
3,2020-11-01 14:53:28.377359,Utility,Phone,222.7563175805277
4,2021-06-05 13:53:28.377359,Books,Flipcart,494.1284923793595
5,2021-07-28 19:53:28.377359,Utility,Electricity,219.94171130968408
6,2021-04-16 11:53:28.377359,Books,Amazon Kindle,270.32259514795845
7,2021-02-15 10:53:28.377359,Food,Zomato,457.1831036346536
8,2021-08-10 19:53:28.377359,Utility,Phone,151.49637259947792
9,2020-11-29 14:53:28.377359,Travel,Auto,443.61888423247854
10,2021-06-15 13:53:28.377359,Travel,Metro,328.1754210974373
11,2021-07-24 13:53:28.377359,Food,Zomato,434.4954675355444
12,2021-07-24 14:53:28.377359,Music,Amazon,329.5360031897569
13,2021-06-06 10:53:28.377359,Utility,Phone,154.0449491816659
14,2021-06-09 13:53:28.377359,Travel,Taxi,485.2977429821982
15,2021-08-24 17:53:28.377359,Food,Zomato,262.9439932340398
16,2021-03-05 19:53:28.377359,Utility,Phone,390.31687619327926
17,2021-04-17 18:53:28.377359,Utility,Electricity,316.8786754246636
18,2021-05-08 15:53:28.377359,Travel,Auto,433.82240427779357
19,2021-05-16 10:53:28.377359,Books,Flipcart,109.32590886550067
20,2020-10-12 18:53:28.377359,Travel,Auto,365.92180825376613
21,2021-01-04 19:53:28.377359,Travel,Metro,329.09737150258513
22,2021-06-24 15:53:28.377359,Food,Zomato,489.1434830522253
23,2020-12-11 10:53:28.377359,Music,Netflix,354.94024099198157
24,2021-05-31 11:53:28.377359,Books,Amazon,498.10049550461065
25,2021-05-21 14:53:28.377359,Food,Hotel,483.315863517772
26,2020-08-26 15:53:28.377359,Books,Amazon Kindle,138.806577801854
27,2021-05-01 15:53:28.377359,Utility,Electricity,103.68079074846585
28,2020-12-14 15:53:28.377359,Utility,Phone,358.4599327957656
29,2021-06-20 10:53:28.377359,Utility,Electricity,184.5577284049955
30,2020-09-15 18:53:28.377359,Food,Swiggy,203.5292397894327
31,2020-09-25 11:53:28.377359,Books,Flipcart,246.50352738452796
32,2021-06-23 11:53:28.377359,Food,Zomato,345.03043608141513
33,2021-05-14 18:53:28.377359,Food,Hotel,449.24802955761743
34,2021-05-14 10:53:28.377359,Utility,Phone,499.8581815222449
35,2021-02-18 18:53:28.377359,Travel,Metro,441.6021430011205
36,2020-12-10 10:53:28.377359,Travel,Auto,472.94143917262176
37,2021-04-18 16:53:28.377359,Music,Amazon,266.0690783774673
38,2021-08-15 10:53:28.377359,Travel,Auto,494.1243994056571
39,2021-05-17 17:53:28.377359,Food,Swiggy,112.33316019807455
40,2021-07-19 12:53:28.377359,Food,Swiggy,291.54598801930536
41,2021-02-20 19:53:28.377359,Utility,Phone,425.18719068071806
42,2021-08-22 17:53:28.377359,Food,Hotel,210.25626950078572
43,2020-09-21 12:53:28.377359,Utility,Phone,486.03393276160733
44,2020-12-26 19:53:28.377359,Utility,Electricity,257.92759337085425
45,2021-05-27 16:53:28.377359,Utility,Electricity,154.74287259516655
46,2021-05-15 15:53:28.377359,Utility,Electricity,359.3249716537848
47,2020-10-28 10:53:28.377359,Books,Flipcart,310.408610004679
48,2021-08-23 17:53:28.377359,Utility,Electricity,310.05840961423314
49,2021-03-16 09:53:28.377359,Music,spotify,232.30340219121138
50,2020-12-24 11:53:28.377359,Food,Zomato,463.00187492635547
51,2020-12-22 17:53:28.377359,Food,Zomato,331.22702332837093
52,2021-03-26 09:53:28.377359,Travel,Taxi,403.6100701341934
53,2021-01-27 09:53:28.377359,Utility,Electricity,183.1866624101276
54,2020-11-16 10:53:28.377359,Music,spotify,160.81754340768396
55,2021-01-21 19:53:28.377359,Books,Flipcart,423.74970808720553
56,2021-05-19 18:53:28.377359,Utility,Phone,319.3428762684619
57,2021-07-15 15:53:28.377359,Utility,Phone,279.6090437716363
58,2021-05-20 10:53:28.377359,Food,Hotel,255.8710346734312
59,2020-08-28 11:53:28.377359,Food,Swiggy,208.2329120852039
60,2021-01-17 11:53:28.377359,Utility,Electricity,382.5195101154448
61,2021-02-25 13:53:28.377359,Food,Hotel,124.65827844174062
62,2021-01-27 19:53:28.377359,Books,Amazon Kindle,497.7708601564023
63,2021-05-10 11:53:28.377359,Travel,Taxi,355.9890502253258
64,2021-01-31 14:53:28.377359,Food,Zomato,232.2223798622789
65,2020-10-23 18:53:28.377359,Music,Netflix,188.7487426895118
66,2020-10-09 16:53:28.377359,Food,Swiggy,263.9577700340145
67,2021-07-31 14:53:28.377359,Music,Netflix,324.786916846731
68,2020-08-26 09:53:28.377359,Travel,Taxi,279.1478844739421
69,2020-10-10 15:53:28.377359,Utility,Electricity,300.52462041935115
70,2021-08-17 13:53:28.377359,Utility,Phone,125.22977317126336
71,2021-03-30 12:53:28.377359,Food,Swiggy,245.36050838040904
72,2021-06-30 18:53:28.377359,Books,Amazon,294.66286899004876
73,2021-08-15 17:53:28.377359,Travel,Metro,117.58872931045573
74,2021-03-20 11:53:28.377359,Travel,Taxi,303.05542098520453
75,2021-03-03 12:53:28.377359,Food,Hotel,425.6252909948148
76,2020-11-17 09:53:28.377359,Music,Netflix,197.5346000167895
77,2021-01-18 14:53:28.377359,Books,Amazon Kindle,482.1523430204321
78,2020-09-09 16:53:28.377359,Music,spotify,415.3728938035302
79,2021-08-17 09:53:28.377359,Music,Netflix,321.7634156544651
80,2021-02-17 09:53:28.377359,Food,Swiggy,283.09570727160764
81,2020-10-29 16:53:28.377359,Food,Hotel,470.08099539923614
82,2020-09-22 09:53:28.377359,Music,spotify,411.14270120842224
83,2021-03-18 09:53:28.377359,Books,Flipcart,451.5844070294999
84,2020-09-21 10:53:28.377359,Music,Netflix,158.7936457269333
85,2021-01-12 09:53:28.377359,Music,Amazon,130.37490757527
86,2021-05-07 16:53:28.377359,Food,Zomato,198.450671792638
87,2021-05-19 15:53:28.377359,Food,Zomato,378.82064134052473
88,2021-04-18 09:53:28.377359,Utility,Phone,124.2212478444578
89,2021-04-12 14:53:28.377359,Music,Amazon,218.487173429263
90,2020-12-01 14:53:28.377359,Music,Amazon,101.57327588889417
91,2021-01-22 17:53:28.377359,Food,Hotel,232.66346838787223
92,2021-01-12 19:53:28.377359,Travel,Taxi,356.8426379886326
93,2021-01-11 09:53:28.377359,Utility,Electricity,111.72080867898062
94,2021-01-04 13:53:28.377359,Utility,Phone,431.1855366816298
95,2021-07-19 13:53:28.377359,Utility,Phone,388.6712132388421
96,2021-01-12 19:53:28.377359,Books,Flipcart,467.5545618966052
97,2021-03-25 11:53:28.377359,Utility,Phone,320.78943360123816
98,2021-05-13 15:53:28.377359,Travel,Taxi,442.0964693975505
99,2020-10-11 16:53:28.377359,Food,Hotel,100.45550129902665

Here are some usefull special chars used in regular expression

^ - start of text/line
$ - end of text/line
. - any char
* - whatever is before this repetes zero or more times
+ - whatever is before this, repeates 1 or more times
? - whatever is before this, comes 0 or 1 times
{m,n} - whatever is before this comes minimum m times and maximum n times

def transaction_done_between_14_to_15(filename):
    p = re.compile("\d{1,2},\d{4,4}-\d{2,2}-\d{2,2} 14:.+")
    with open(filename) as f:
        for line in f:
            if p.match(line.strip()):
                print(line, end="")

transaction_done_between_14_to_15("wallet.csv")

0,2021-03-07 14:53:28.377359,Music,Amazon,421.2073272347991
3,2020-11-01 14:53:28.377359,Utility,Phone,222.7563175805277
9,2020-11-29 14:53:28.377359,Travel,Auto,443.61888423247854
12,2021-07-24 14:53:28.377359,Music,Amazon,329.5360031897569
25,2021-05-21 14:53:28.377359,Food,Hotel,483.315863517772
64,2021-01-31 14:53:28.377359,Food,Zomato,232.2223798622789
67,2021-07-31 14:53:28.377359,Music,Netflix,324.786916846731
77,2021-01-18 14:53:28.377359,Books,Amazon Kindle,482.1523430204321
89,2021-04-12 14:53:28.377359,Music,Amazon,218.487173429263
90,2020-12-01 14:53:28.377359,Music,Amazon,101.57327588889417