Develop Reference

Pandas df.loc with regex

I'm working with a data set consisting of several csv files of nearly the same form. Each csv describes a particular date, and labels the data by state/province. However, the format of one of the column headers in the data set was altered from Province/State to Province_State, so that all csv's created before a certain date use the first format and all csv's created after that date use the second format.
I'm trying to sum up all the entries corresponding to a particular state. At present, the code I'm working with is as follows:
daily_data.loc[daily_data[areaLabel] == location].sum()
where daily_data is the dataframe containing the csv data, location is the name of the state I'm looking for, and arealabel is a variable storing either 'Province/State' or 'Province_State' depending on the result of a date check. I would like to eliminate the date check by e.g. conditioning on a regular expression like Province(/|_)State, but I'm having a lot of trouble finding a way to index into a pandas dataframe by regular expression. Is this doable (and in a way that would make the code more elegant rather than less elegant)? If so, I'd appreciate it if someone could point me in the right direction.

Use filter to get the columns that match your regex
>>> df.filter(regex="Province(/|_)State").columns[0]
'Province/State'
Then use this to select only rows that match your location:
df[df[df.filter(regex="Province(/|_)State").columns[0]]==location].sum()
This however assumes that there are no other columns that would match the regex.

Python: create new columns from rows based on multiple conditions

I've been poking around a bit and can't see to find a close solution to this one:
I'm trying to transform a dataframe from this:
To this:
Such that remark_code_names with similar denial_amounts are provided new columns based on their corresponding har_id and reason_code_name.
I've tried a few things, including a groupby function, which gets me halfway there.
denials.groupby(['har_id','reason_code_name','denial_amount']).count().reset_index()
But this obviously leaves out the reason_code_names that I need.
Here's a minimum:
pd.DataFrame({'har_id':['A','A','A','A','A','A','A','A','A'],'reason_code_name':[16,16,16,16,16,16,16,22,22],
'remark_code_name':['MA04','N130','N341','N362','N517','N657','N95','MA04','N341'],
'denial_amount':[5402,8507,5402,8507,8507,8507,8507,5402,5402]})

Using groupby() is a good way to go. Use it along with transform() and overwrite the column with name 'remark_code_name. This solution puts all remark_code_names together in the same column.
denials['remark_code_name'] = denials.groupby(['har_id','reason_code_name','denial_amount'])['remark_code_name'].transform(lambda x : ' '.join(x))
denials.drop_duplicates(inplace=True)
If you really need to create each code in their own columns, you could apply another function and use .split(). However you will first need to set the number of columns depending on the max number of codes you find in a single row.

How to use parse from phonenumbers Python library on a pandas data frame?

How can I parse phone numbers from a pandas data frame, ideally using phonenumbers library?
I am trying to use a port of Google's libphonenumber library on Python,
https://pypi.org/project/phonenumbers/.
I have a data frame with 3 million phone numbers from many countries. I have a row with the phone number, and a row with the country/region code. I'm trying to use the parse function in the package. My goal is to parse each row using the corresponding country code but I can't find a way of doing it efficiently.
I tried using apply but it didn't work. I get a "(0) Missing or invalid default region." error, meaning it won't pass the country code string.
df['phone_number_clean'] = df.phone_number.apply(lambda x:
phonenumbers.parse(str(df.phone_number),str(df.region_code)))
The line below works, but doesn't get me what I want, as the numbers I have come from about 120+ different countries.
df['phone_number_clean'] = df.phone_number.apply(lambda x:
phonenumbers.parse(str(df.phone_number),"US"))
I tried doing this in a loop, but it is terribly slow. Took me more than an hour to parse 10,000 numbers, and I have about 300x that:
for i in range(n):
df3['phone_number_std'][i] =
phonenumbers.parse(str(df.phone_number[i]),str(df.region_code[i]))
Is there a method I'm missing that could run this faster? The apply function works acceptably well but I'm unable to pass the data frame element into it.
I'm still a beginner in Python, so perhaps this has an easy solution. But I would greatly appreciate your help.

Your initial solution using apply is actually pretty close - you don't say what doesn't work about it, but the syntax for a lambda function over multiple columns of a dataframe, rather than on the rows within a single column, is a bit different. Try this:
df['phone_number_clean'] = df.apply(lambda x:
phonenumbers.parse(str(x.phone_number),
str(x.region_code)),
axis='columns')
The differences:
You want to include multiple columns in your lambda function, so you want to apply your lambda function to the entire dataframe (i.e, df.apply) rather than to the Series (the single column) that is returned by doing df.phone_number.apply. (print the output of df.phone_number to the console - what is returned is all the information that your lambda function will be given).
The argument axis='columns' (or axis=1, which is equivalent, see the docs) actually slices the data frame by rows, so apply 'sees' one record at a time (ie, [index0, phonenumber0, countrycode0], [index1, phonenumber1, countrycode1]...) as opposed to slicing the other direction, which would give it ([phonenumber0, phonenumber1, phonenumber2...])
Your lambda function only knows about the placeholder x, which, in this case, is the Series [index0, phonenumber0, countrycode0], so you need to specify all the values relative to the x that it knows - i.e., x.phone_number, x.country_code.

Love the solution of #katelie, but here's my code. Added a try/except block to skip the format_number function from failing. It cannot handle strings that are too long.
import phonenumber as phon
def formatE164(self):
try:
return phon.format_number(phon.parse(str(self),"NL"),phon.PhoneNumberFormat.E164)
except:
pass
df['column'] = df['column'].apply(formatE164)

how to write an empty column in a csv based on other columns in the same csv file

I don't know whether this is a very simple qustion, but I would like to do a condition statement based on two other columns.
I have two columns like: the age and the SES and the another empty column which should be based on these two columns. For example when one person is 65 years old and its corresponding socio-economic status is high, then in the third column(empty column=vitality class) a value of 1 is for example given. I have got an idea about what I want to achieve, however I have no idea how to implement that in python itself. I know I should use a for loop and I know how to write conditons, however due to the fact that I want to take two columns into consideration for determining what will be written in the empty column, I have no idea how to write that in a function
and furthermore how to write back into the same csv (in the respective empty column)
[]

Use the pandas module to import the csv as a DataFrame object. Then you can do logical statements to fill empty columns:
import pandas as pd
df = pd.read_csv('path_to_file.csv')
df.loc[(df['age']==65) & (df['SES']=='high'), 'vitality_class'] = 1
df.to_csv('path_to_new_file.csv', index=False)

appending non-unique rows to another database using python

Hey all,
I have two databases. One with 145000 rows and approx. 12 columns. I have another database with around 40000 rows and 5 columns. I am trying to compare based on two columns values. For example if in CSV#1 column 1 says 100-199 and column two says Main St(meaning that this row is contained within the 100 block of main street), how would I go about comparing that with a similar two columns in CSV#2. I need to compare every row in CSV#1 to each single row in CSV#2. If there is a match I need to append the 5 columns of each matching row to the end of the row of CSV#2. Thus CSV#2's number of columns will grow significantly and have repeat entries, doesnt matter how the columns are ordered. Any advice on how to compare two columns with another two columns in a separate database and then iterate across all rows. I've been using python and the import csv so far with the rest of the work, but this part of the problem has me stumped.
Thanks in advance
-John

A csv file is NOT a database. A csv file is just rows of text-chunks; a proper database (like PostgreSQL or Mysql or SQL Server or SQLite or many others) gives you proper data types and table joins and indexes and row iteration and proper handling of multiple matches and many other things which you really don't want to rewrite from scratch.
How is it supposed to know that Address("100-199")==Address("Main Street")? You will have to come up with some sort of knowledge-base which transforms each bit of text into a canonical address or address-range which you can then compare; see Where is a good Address Parser but be aware that it deals with singular addresses (not address ranges).
Edit:
Thanks to Sven; if you were using a real database, you could do something like
SELECT
User.firstname, User.lastname, User.account, Order.placed, Order.fulfilled
FROM
User
INNER JOIN Order ON
User.streetnumber=Order.streetnumber
AND User.streetname=Order.streetname
if streetnumber and streetname are exact matches; otherwise you still need to consider point #2 above.