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How to add a column based on the values of a list of lists in python? No libraries

How to add a column based on one of the values of a list of lists in python?
I have the following list and I need to add a new column based on the value of Currency.
If Pound , Euro = Amount *0.9
If USD , Euro =Amount *1.2
I need to code without libraries.
[['Buyer', 'Seller', 'Amount', 'Property_Type','Currency'],
['100', '200', '4923', 'c', 'Pound'],
['600', '429', '838672', 'a', 'USD'],
['650', '400', '8672', 'a', 'Euro']
Result
[['Buyer', 'Seller', 'Amount', 'Property_Type', 'Currency', 'Euro'],
['100', '200', '5000', 'c', 'Livre', '6000'],
['600', '429', '10000', 'a', 'USD', '9000'],
['650', '400', '8600', 'a', 'Euro', '8600']
Thank you very much, any readings on how to import a csv and manipulate it, without libraries, would be much appreciated.

Assuming the columns are always in the same order...
EXCH_RATES = {
'Pound': Decimal('0.9'),
'USD': Decimal('1.2'),
'Euro': 1,
}
rows[0].append('Euro')
for row in rows[1:]:
exch_rate = EXCH_RATES[row[4]]
row.append(str(exch_rate * Decimal[row[2]]))

Check the last item in the list inside the list then check what its currency is then change the amount like so:
lst = [['Buyer', 'Seller', 'Amount', 'Property_Type','Currency'], ['100', '200', '4923', 'c', 'Pound'], ['600', '429', '838672', 'a', 'USD'], ['650', '400', '8672', 'a', 'Euro']]
for i in range(3):
if lst[i][3] == 'Pound':
lst[i].append(str(int(lst[i][2]) * 0.9))
elif lst[i][3] == 'USD':
lst[i].append(str(int(lst[i][2]) * 1.2))
else:
lst[i].append(lst[i][2])
Although you would be better storing the data in a csv file but then you would have to use the csv library.
Tell me if this helps and if you want to use the csv library tell me so I can tell you how to use it.

Python csv file convert string to float

I have a csv file full of data, which is all type string. The file is called Identifiedλ.csv.
Here is some of the data from the csv file:
['Ref', 'Ion', 'ULevel', 'UConfig.', 'ULSJ', 'LLevel', 'LConfig.', 'LLSJ']
['12.132', 'Ne X', '4', '2p1', '2P3/2', '1', '1s1', '1S0']
['12.132', 'Ne X', '3', '2p1', '2P3/2', '1', '1s1', '1S0']
['12.846', 'Fe XX', '58', '1s2.2s2.2p2.3d1', '4P5/2', '1', '1s2.2s2.2p3', '4S3/2']
What I would like to do is the read the file and search for a number in the column 'Ref', for example 12.846. And if the number I search matches a number in the file, print the whole row of that number .
eg. something like:
csv_g = csv.reader(open('Identifiedλ.csv', 'r'), delimiter=",")
for row in csv_g:
if 12.846 == (row[0]):
print (row)
And it would return (hopefully)
['12.846', 'Fe XX', '58', '1s2.2s2.2p2.3d1', '4P5/2', '1', '1s2.2s2.2p3', '4S3/2']
However this returns nothing and I think it's because the 'Ref' column is type string and the number I search is type float. I'm trying to find a way to convert the string to float but am seemingly stuck.
I've tried:
df = pd.read_csv('Identifiedλ.csv', dtype = {'Ref': np.float64,})
and
array = b = np.asarray(array,
dtype = np.float64, order = 'C')
but am confused on how to incorporate this with the rest of the search.
Any tips would be most helpful! Thank you!

Python has a function to convert strings to floats. For example, the following evaluates to True:
float('3.14')==3.14
I would try this conversion while comparing the value in the first column.

How do you convert formatted 'epi-week' to date using Python?

I am currently trying to learn how to apply Data Science skills which I am learning through Coursera and Dataquest to little personal projects.
I found a dataset on Google BigQuery from the US Department of Health and Human Services which includes all weekly surveillance reports of nationally notifiable diseases for all U.S. cities and states published between 1888 and 2013.
I exported the data to a .csv file and imported it into a Jupyter notebook which I am running through Anaconda. Upon looking at the header of the dataset I noticed that the dates/weeks are shown as 'epi_week'.
I am trying to make the data more readable and useable for some analysis, to do this I was hoping to conver it into something along the lines of DD/MM/YYYY or Week/Month/Year etc.
I did some research, apparently epi-weeks are also referred to as CDC weeks and so far I found an extension/package for python 3 which is called "epiweeks".
Using the epiweeks package I can turn some 'normal' dates into what the package creator refers to into some sort of an epi weeks form but they look nothing like what I can see in the dataset.
For example if I use todays date, the 24th of May 2019 (24/05/2019) then the output is: "Week 21 of Year 2019" but this is what the first four entrys in the data (and following the same format, all the other ones) look like:
epi_week
'197006'
'197007'
'197008'
'197012'
In [1]: disease_header
Out [1]:
[['epi_week', 'state', 'loc', 'loc_type', 'disease', 'cases', 'incidence_per_100000']]
In [2]: disease[:4]
Out [2]:
[['197006', 'AK', 'ALASKA', 'STATE', 'MUMPS', '0', '0'],
['197007', 'AK', 'ALASKA', 'STATE', 'MUMPS', '0', '0'],
['197008', 'AK', 'ALASKA', 'STATE', 'MUMPS', '0', '0'],
['197012', 'AK', 'ALASKA', 'STATE', 'MUMPS', '0', '0']]

The epiweeks package was developed to solve problems like the one you have here.
With the example data you provided, let's create a new column with week ending date:
import pandas as pd
from epiweeks import Week
columns = ['epi_week', 'state', 'loc', 'loc_type',
'disease', 'cases', 'incidence_per_100000']
data = [
['197006', 'AK', 'ALASKA', 'STATE', 'MUMPS', '0', '0'],
['197007', 'AK', 'ALASKA', 'STATE', 'MUMPS', '0', '0'],
['197008', 'AK', 'ALASKA', 'STATE', 'MUMPS', '0', '0'],
['197012', 'AK', 'ALASKA', 'STATE', 'MUMPS', '0', '0']
]
df = pd.DataFrame(data, columns=columns)
# Now create a new column with week ending date in ISO format
df['week_ending'] = df['epi_week'].apply(lambda x: Week.fromstring(x).enddate())
That results in something like:
I recommend you to have a look over the epiweeks package documentation for more examples.
If you only need to have year and week columns, that can be done without using the epiweeks package:
df['year'] = df['epi_week'].apply(lambda x: int(x[:4]))
df['week'] = df['epi_week'].apply(lambda x: int(x[4:6]))
That results in something like:

Create news columns and add key and values from dictionary in new columns for existing csv file

I have a csv file like this :
id;verbatim
1;je veux manger
2;tu as manger
I have my script which return a dictionary like this :
dico_phrases = {"['manger']": ['7', '1', '0'], "['être', 'laid']": ['0', '21', '1041']}
T would like to add 4 news columns like this :
id;verbatim,key,value1,value2,value3
1;je veux manger
2;tu as manger
And then add my dictionary in each columns like this :
id;verbatim;key,value1,value2,value3
1je veux manger;manger;7;1;0
2;tu as manger;être laid;0;21;1041
Below the script which allow me to get my dictionary :
with open('output.csv','wb') as f:
w = csv.writer(f)
w.writerow(dico_phrases.keys())
w.writerow(dico_phrases.values())
I have this :
['manger'],"['être', 'laid']"
"['7', '1', '0']","['0', '21', '1041']"
It is not quite I have imagined

Consider using pandas for this -
df = pd.read_csv("input.csv", index_col='id')
dico_phrases = {"['manger']": ['7', '1', '0'], "['être', 'laid']": ['0', '21', '1041']}
df['key'] = [" ".join(eval(x)) for x in dico_phrases.keys()]
df = df.join(pd.DataFrame([x for x in dico_phrases.values()], index=df.index))
df.to_csv("output.csv")
Output:
id,verbatim,key,0,1,2
1,je veux manger,manger,7,1,0
2,tu as manger,être laid,0,21,1041

Tab delineated python 3 .txt file reading

I am having a bit of trouble getting started on an assignment. We are issued a tab delineated .txt file with 6 columns of data and around 50 lines of this data. I need help starting a list to store this data in for later recall. Eventually I will need to be able to list all the contents of any particular column and sort it, count it, etc. Any help would be appreciated.
Edit; I really haven't done much besides research on this kinda stuff, I know ill be looking into csv, and i have done single column .txt files before but im not sure how to tackle this situation. How will I give names to the separate columns? how will I tell the program when one row ends and the next begins?

The dataframe structure in Pandas basically does exactly what you want. It's highly analogous to the data frame in R if you're familiar with that. It has built in options for subsetting, sorting, and otherwise manipulating tabular data.
It reads directly from csv and even automatically reads in column names. You'd call:
read_csv(yourfilename,
sep='\t', # makes it tab delimited
header=1) # makes the first row the header row.
Works in Python 3.

Let's say you have a csv like the following.
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
You can read them into a dictionary like so:
>>> import csv
>>> reader = csv.DictReader(open('test.csv','r'), fieldnames= ['col1', 'col2', 'col3', 'col4', 'col5', 'col6'], dialect='excel-tab')
>>> for row in reader:
... print row
{'col6': '6', 'col4': '4', 'col5': '5', 'col2': '2', 'col3': '3', 'col1': '1'}
{'col6': '6', 'col4': '4', 'col5': '5', 'col2': '2', 'col3': '3', 'col1': '1'}
{'col6': '6', 'col4': '4', 'col5': '5', 'col2': '2', 'col3': '3', 'col1': '1'}
{'col6': '6', 'col4': '4', 'col5': '5', 'col2': '2', 'col3': '3', 'col1': '1'}
{'col6': '6', 'col4': '4', 'col5': '5', 'col2': '2', 'col3': '3', 'col1': '1'}
But Pandas library might be better suited for this. http://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files

Sounds like a job better suited to a database. You should just use something like PostgreSQLs COPY FROM operation to import the CSV data into a table then use python + SQL for all your sorting, searching and matching needs.
If you feel a real database is overkill there's still options like SQLlite and BerkleyDB which both have python modules.
EDIT: BerkelyDB is deprecated but anydbm is similiar in concept.

I think using a db for 50 lines and 6 colums is overkill, so here's my idea:
from __future__ import print_function
import os
from operator import itemgetter
def get_records_from_file(path_to_file):
"""
Read a tab-deliminated file and return a
list of dictionaries representing the data.
"""
records = []
with open(path_to_file, 'r') as f:
# Use the first line to get names for columns
fields = [e.lower() for e in f.readline().split('\t')]
# Iterate over the rest of the lines and store records
for line in f:
record = {}
for i, field in enumerate(line.split('\t')):
record[fields[i]] = field
records.append(record)
return records
if __name__ == '__main__':
path = os.path.join(os.getcwd(), 'so.txt')
records = get_records_from_file(path)
print('Number of records: {0}'.format(len(records)))
s = sorted(records, key=itemgetter('id'))
print('Sorted: {0}'.format(s))
For storing records for later use, look into Python's pickle library--that'll allow you to preserve them as Python objects.
Also, note I don't have Python 3 installed on the computer I'm using right now, but I'm pretty sure this'll work on Python 2 or 3.