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Convert complex comma-separated string into Python dictionary

I am getting following string format from csv file in Pandas
"title = matrix, genre = action, year = 2000, rate = 8"
How can I change the string value into a python dictionary like this:
movie = "title = matrix, genre = action, year = 2000, rate = 8"
movie = {
"title": "matrix",
"genre": "action",
"year": "1964",
"rate":"8"
}

You can split the string and then convert it into a dictionary.
A sample code is given below
movie = "title = matrix, genre = action, year = 2000, rate = 8"
movie = movie.split(",")
# print(movie)
tempMovie = [i.split("=") for i in movie]
movie = {}
for i in tempMovie:
movie[i[0].strip()] = i[1].strip()
print(movie)

For the solution you can use regex
import re
input_user = "title = matrix, genre = action, year = 2000, rate = 8"
# Create a pattern to match the key-value pairs
pattern = re.compile(r"(\w+) = ([\w,]+)" )
# Find all matches in the input string
matches = pattern.findall(input_user)
# Convert the matches to a dictionary
result = {key: value for key, value in matches}
print(result)
The result:
{'title': 'matrix,', 'genre': 'action,', 'year': '2000,', 'rate': '8'}
I hope this can solve your problem.

movie = "title = matrix, genre = action, year = 2000, rate = 8"
dict_all_movies = {}
for idx in df.index:
str_movie = df.at[idx, str_movie_column]
movie_dict = dict(item.split(" = ") for item in str_movie.split(", "))
dict_all_movies[str(idx)] = movie_dict

Creating multiple dataframe using loop or function

I'm trying to extract the hash rate for 3 cryptocurrencies and I have attached the code for the same below. Now, I want to pass three urls and in return I need three different different dictionaries which should have the values. I'm stuck and I don't understand how should I go about it. I have tried using loops but it is not working out for me.
url = {'Bitcoin' : 'https://bitinfocharts.com/comparison/bitcoin-hashrate.html#3y',
'Ethereum': 'https://bitinfocharts.com/comparison/ethereum-hashrate.html#3y',
'Litecoin': 'https://bitinfocharts.com/comparison/litecoin-hashrate.html'}
for ele in url:
#### requesting the page and extracting the script which has date and values
session = requests.Session()
page = session.get(ele[i])
soup = BeautifulSoup(page.content, 'html.parser')
values = str(soup.find_all('script')[4])
values = values.split('d = new Dygraph(document.getElementById("container"),')[1]
#create an empty dict to append date and hashrates
dict([("crypto_1 %s" % i,[]) for i in range(len(url))])
#run a loop over all the dates and adding to dictionary
for i in range(values.count('new Date')):
date = values.split('new Date("')[i+1].split('"')[0]
value = values.split('"),')[i+1].split(']')[0]
dict([("crypto_1 %s" % i)[date] = value

You can use next example how to get data from all 3 URLs and create a dataframe/dictionary from it:
import re
import requests
import pandas as pd
url = {
"Bitcoin": "https://bitinfocharts.com/comparison/bitcoin-hashrate.html#3y",
"Ethereum": "https://bitinfocharts.com/comparison/ethereum-hashrate.html#3y",
"Litecoin": "https://bitinfocharts.com/comparison/litecoin-hashrate.html",
}
data = []
for name, u in url.items():
html_doc = requests.get(u).text
for date, hash_rate in re.findall(
r'\[new Date\("(.*?)"\),(.*?)\]', html_doc
):
data.append(
{
"Name": name,
"Date": date,
"Hash Rate": float("nan")
if hash_rate == "null"
else float(hash_rate),
}
)
df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
# here save df to CSV
# this will create a dictionary, where the keys are crypto names and values
# are dicts with keys Date/HashRate:
out = {}
for name, g in df.groupby("Name"):
out[name] = g[["Date", "Hash Rate"]].to_dict(orient="list")
print(out)
Prints:
{
"Bitcoin": {
"Date": [
Timestamp("2009-01-03 00:00:00"),
Timestamp("2009-01-04 00:00:00"),
Timestamp("2009-01-05 00:00:00"),
...

How to fill column with values based on string contains condition

I have the following data:
data = {
'employee' : ['Emp1', 'Emp2', 'Emp3', 'Emp4', 'Emp5'],
'code' : ['2018_1', '2018_3', '2019_1', '2019_2', '2017_1'],
}
old_salary_bonus = 3000
new_salary_bonus = {
'2019_1': 1000,
'2019_2': 980,
}
df = pd.DataFrame(data)
Task: Add df['salary_bonus'] column based on the following condition:
If employee's code contains '2019', use code value to retrieve salary bonus value from new_salary_bonus, else use old_salary_bonus value.
Expected Output:
employee code salary_bonus
0 Emp1 2018_1 3000
1 Emp2 2018_3 3000
2 Emp3 2019_1 1000
3 Emp4 2019_2 980
4 Emp5 2017_1 3000
Please help.

Use Series.map with Series.fillna for repplace non matched values:
import pandas as pd
data = {
'employee' : ['Emp1', 'Emp2', 'Emp3', 'Emp4', 'Emp5'],
'code' : ['2018_1', '2018_3', '2019_1', '2019_2', '2017_1'],
}
old_salary_bonus = 3000
new_salary_bonus = {
'2019_1': 1000,
'2019_2': 980,
}
df = pd.DataFrame(data)
df['salary_bonus'] = df['code'].map(new_salary_bonus).fillna(old_salary_bonus)
print (df)
employee code salary_bonus
0 Emp1 2018_1 3000.0
1 Emp2 2018_3 3000.0
2 Emp3 2019_1 1000.0
3 Emp4 2019_2 980.0
4 Emp5 2017_1 3000.0
Another solution with get with default value if not matched:
df['salary_bonus'] = df['code'].map(lambda x: new_salary_bonus.get(x, old_salary_bonus))

You can use the code below:
df['salary_bonus'] = old_salary_bonus
df.loc[df['code'].isin(list(new_salary_bonus)), 'salary_bonus'] = list(new_salary_bonus.values())

How can I put excel data to the dictionary?

I wanna put excel data to the dictionary.
Excel is
views.py is
#coding:utf-8
from django.shortcuts import render
import xlrd
book3 = xlrd.open_workbook('./data/excel.xlsx')
sheet3 = book3.sheet_by_index(0)
large_item = None
data_dict = {}
for row_index in range(1,sheet3.nrows):
rows3 = sheet3.row_values(row_index)
large_item = rows3[1] or large_item
data_dict = rows3
Now when I printed out print(data_dict),['', '4', '10', 'Karen', ''] was shown.Before,I wrote data_dict.extend(rows3) in place of data_dict = rows3,but in that time dict has not extend error happens.My ideal output is
data_dict = {
1: {
user_id: 1,
name_id: 1,
name: Blear,
age: 40,
man: false,
employee: leader,
},
2: {
user_id: 2,
name_id: 5,
・
　　　　　　 ・
　　　　　　 ・
},
・
　　　　　　 ・
　　　　　　 ・
}
How should I write to achieve my goal?

Your problem is :
data_dict = rows3
This doesn't add rows3 to data_dict, this set is value. So data_dict is equal to the last row.
To add element to a dict you need to do this:
data_dict[KEY] = VALUE
Your key will be the row index.
Now, you want another dict like VALUE
{
user_id: 1,
name_id: 1,
name: Blear,
age: 40,
man: false,
employee: leader,
}
So for each row you need to construct this dict, use headers and cell value to do it.
I don't test this code, it's just to give you an idea to how to do it.
#coding:utf-8
from django.shortcuts import render
import xlrd
book3 = xlrd.open_workbook('./data/excel.xlsx')
sheet3 = book3.sheet_by_index(0)
headers = sheet3.row_values(0)
large_item = None
data_dict = {}
for row_index in range(1,sheet3.nrows):
rows3 = sheet3.row_values(row_index)
large_item = rows3[1] or large_item
# Create dict with headers and row values
row_data = {}
for idx_col,value in enumerate(rows3):
header_value = headers[idx_col]
# Avoid to add empty column. A column in your example
if header_value:
row_data[headers[idx_col]] = value
# Add row_data to your data_dict with
data_dict[row_index] = row_data

You can use python's library pandas for an easy solution:
from pandas import *
xls = ExcelFile('your_excel_file.xls')
df = xls.parse(xls.sheet_names[0])
df.to_dict()

Build Nested Dictionary at run time in Python

I want to build dynamic dictionary in python like this,,
users = {log_id : { "message_id" : "1", "sent_to" : "taqi.official#outlook.com" , "unique_arguments" : "03455097679"},
log_id : { "message_id" : "1", "sent_to" : "taqi.hass#cogilent.com" , "unique_arguments" : "03455097679" },
log_id : { "message_id" : "2 Turab", "sent_to" : "taqi.official#gmailllllll.com" , "unique_arguments" : "4534534535" }}
I have write this code but it is not building as i desired;
cur = conn.cursor()
cur.execute("select log_id, message_id, sent_to, unique_arguments from sendmessage_log_messages where log_status = 'Queue'")
rows = cur.fetchall()
count = 0
for row in rows:
temp['message_id'] = row[1]
temp['sent_to'] = str(row[2])
temp['unique_arguments'] = row[3]
log_dictionary[row[0]] = temp
print log_dictionary
It produces the this output,
{1: {'unique_arguments': 'log_8_taqi.official#gmailllllll.com', 'message_id': 8, 'sent_to': 'taqi.official#gmailllllll.com'},
2: {'unique_arguments': 'log_8_taqi.official#gmailllllll.com', 'message_id': 8, 'sent_to': 'taqi.official#gmailllllll.com'},
3: {'unique_arguments': 'log_8_taqi.official#gmailllllll.com', 'message_id': 8, 'sent_to': 'taqi.official#gmailllllll.com'},
4: {'unique_arguments': 'log_8_taqi.official#gmailllllll.com', 'message_id': 8, 'sent_to': 'taqi.official#gmailllllll.com'}}

Since no answer is given here, I am just giving the explanation here.
Here, you are replacing the temp dictionary again and again. When you do log_dictionary[row[0]] = temp, this just points to the existing temp dictionary. Thus whenever you change any value in the temp dictionary and you read the log_dictionary, this will always give you the updated temp dictionary only.
Try this:
for row in rows:
temp['message_id'] = row[1]
temp['sent_to'] = str(row[2])
temp['unique_arguments'] = row[3]
log_dictionary[row[0]] = temp
print log_dictionary
temp['message_id'] = 0
temp['send_to'] = 'stackoverflow'
temp['unique_arguments'] = None
print log_dictionary
This will give you the updated temp dictionary values as it is just referenced with the log_dictionary. As Ashwini Chaudhary mentioned in your comment, if you initialize a temp dictionary within the for loop before referencing it to log_dictionary, you will get the desired values. (eg)
for row in rows:
temp = {}
temp['message_id'] = row[1]
temp['sent_to'] = str(row[2])
temp['unique_arguments'] = row[3]
log_dictionary[row[0]] = temp