Develop Reference

Python dataframe from 2 text files (different number of columns)

I need to make a dataframe from two txt files.
The first txt file looks like this Street_name space id.
The second txt file loks like this City_name space id.
Example:
text file 1:
Roseberry st 1234
Brooklyn st 4321
Wolseley 1234567
text file 2:
Winnipeg 4321
Winnipeg 1234
Ste Anne 1234567
I need to make one dataframe out of this. Sometimes there is just one word for Street_name, and sometimes more. The same goes for City_name.
I get an error: ParserError: Error tokenizing data. C error: Expected 2 fields in line 5, saw 3 because I'm trying to put both words for street name into the same column, but don't know how to do it. I want one column for street name (no matter if it consists of one or more words, one for city name and one for id.
I want a df with 3 rows and 3 cols.
Thanks!
Edit: both text files are huge (each 50 mil rows +) so i need this code not to break and be optimised for large files.

It is NOT correct CSV and it may need to read it on your own.
You can normal open(), read() and later split on new line to create list of lines. And later you can use for-loop and use line.rsplit(" ", 1) to split line on last space.
Minimal working example:
I use io to simulate file in memory - so everyone can simply copy and test it - but you should use open()
text = '''Roseberry st 1234
Brooklyn st 4321
Wolseley 1234567'''
import io
#with open('filename') as fh:
with io.StringIO(text) as fh:
lines = fh.read().splitlines()
print(lines)
lines = [line.rsplit(" ", 1) for line in lines]
print(lines)
import pandas as pd
df = pd.DataFrame(lines, columns=['name', 'name'])
print(df)
Result:
['Roseberry st 1234', 'Brooklyn st 4321', 'Wolseley 1234567']
[['Roseberry st', '1234'], ['Brooklyn st', '4321'], ['Wolseley', '1234567']]
name number
0 Roseberry st 1234
1 Brooklyn st 4321
2 Wolseley 1234567
EDIT:
read_csv can use regex to define separator (i.e. sep="\s+" for many spaces) and it can even use lookahead/loopbehind ((?=...)/(?<=...)) to check if there is digit after space without catching it as part of separator.
text = '''Roseberry st 1234
Brooklyn st 4321
Wolseley 1234567'''
import io
import pandas as pd
#df = pd.read_csv('filename', names=['name', 'number'], sep='\s(?=\d)', engine='python')
df = pd.read_csv(io.StringIO(text), names=['name', 'number'], sep='\s(?=\d)', engine='python')
print(df)
Result:
name number
0 Roseberry st 1234
1 Brooklyn st 4321
2 Wolseley 1234567
And later you can try to connect both dataframe using .join(), .merge() with parameter on= (or something similar) like in SQL query.
text1 = '''Roseberry st 1234
Brooklyn st 4321
Wolseley 1234567'''
text2 = '''Winnipeg 4321
Winnipeg 1234
Ste Anne 1234567'''
import io
import pandas as pd
df1 = pd.read_csv(io.StringIO(text1), names=['street name', 'id'], sep='\s(?=\d)', engine='python')
df2 = pd.read_csv(io.StringIO(text2), names=['city name', 'id'], sep='\s(?=\d)', engine='python')
print(df1)
print(df2)
df = df1.merge(df2, on='id')
print(df)
Result:
street name id
0 Roseberry st 1234
1 Brooklyn st 4321
2 Wolseley 1234567
city name id
0 Winnipeg 4321
1 Winnipeg 1234
2 Ste Anne 1234567
street name id city name
0 Roseberry st 1234 Winnipeg
1 Brooklyn st 4321 Winnipeg
2 Wolseley 1234567 Ste Anne
Pandas doc: Merge, join, concatenate and compare

There's nothing that I'm aware of in pandas that does this automatically.
Below, I built a script that will merge those addresses (addy + st) into a single column, then merges the two data frames into one based on the "id".
I assume your actual text files are significantly larger, so assuming they follow the pattern set in the two examples, this script should work fine.
Basically, this code turns each line of text in the file into a list, then combines lists of length 3 into length 2 by combining the first two list items.
After that, it turns the "list of lists" into a dataframe and merges those dataframes on column "id".
Couple caveats:
Make sure you set the correct text file paths
Make sure the first line of the text files contains 2, single string column headers (ie: "address id") or (ie: "city id")
Make sure each text file id column header is named "id"
import pandas as pd
import numpy as np
# set both text file paths (you may need full path i.e. C:\Users\Name\bla\bla\bla\text1.txt)
text_path_1 = r'text1.txt'
text_path_2 = r'text2.txt'
# declares first text file
with open(text_path_1) as f1:
text_file_1 = f1.readlines()
# declares second text file
with open(text_path_2) as f2:
text_file_2 = f2.readlines()
# function that massages data into two columns (to put "st" into same column as address name)
def data_massager(text_file_lines):
data_list = []
for item in text_file_lines:
stripped_item = item.strip('\n')
split_stripped_item = stripped_item.split(' ')
if len(split_stripped_item) == 3:
split_stripped_item[0:2] = [' '.join(split_stripped_item[0 : 2])]
data_list.append(split_stripped_item)
return data_list
# runs function on both text files
data_list_1 = data_massager(text_file_1)
data_list_2 = data_massager(text_file_2)
# creates dataframes on both text files
df1 = pd.DataFrame(data_list_1[1:], columns = data_list_1[0])
df2 = pd.DataFrame(data_list_2[1:], columns = data_list_2[0])
# merges data based on id (make sure both text files' id is named "id")
merged_df = df1.merge(df2, how='left', on='id')
# prints dataframe (assuming you're using something like jupyter-lab)
merged_df

pandas has strong support for strings. You can make the lines of each file into a Series and then use a regular expression to separate the fields into separate columns. I assume that "id" is the common value that links the two datasets, so it can become the dataframe index and the columns can just be added together.
import pandas as pd
street_series = pd.Series([line.strip() for line in open("text1.txt")])
street_df = street_series.str.extract(r"(.*?) (\d+)$")
del street_series
street_df.rename({0:"street", 1:"id"}, axis=1, inplace=True)
street_df.set_index("id", inplace=True)
print(street_df)
city_series = pd.Series([line.strip() for line in open("text2.txt")])
city_df = city_series.str.extract(r"(.*?) (\d+)$")
del city_series
city_df.rename({0:"city", 1:"id"}, axis=1, inplace=True)
city_df.set_index("id", inplace=True)
print(city_df)
street_df["city"] = city_df["city"]
print(street_df)

Extract values within the quotes signs into two separate columns with python

How can i extract the values within the quotes signs into two separate columns with python. The dataframe is given below:
df = pd.DataFrame(["'FRH02';'29290'", "'FRH01';'29300'", "'FRT02';'29310'", "'FRH03';'29340'",
"'FRH05';'29350'", "'FRG02';'29360'"], columns = ['postcode'])
df
postcode
0 'FRH02';'29290'
1 'FRH01';'29300'
2 'FRT02';'29310'
3 'FRH03';'29340'
4 'FRH05';'29350'
5 'FRG02';'29360'
i would like to get an output like the one below:
postcode1 postcode2
FRH02 29290
FRH01 29300
FRT02 29310
FRH03 29340
FRH05 29350
FRG02 29360
i have tried several str.extract codes but havent been able to figure this out. Thanks in advance.

Finishing Quang Hoang's solution that he left in the comments:
import pandas as pd
df = pd.DataFrame(["'FRH02';'29290'",
"'FRH01';'29300'",
"'FRT02';'29310'",
"'FRH03';'29340'",
"'FRH05';'29350'",
"'FRG02';'29360'"],
columns = ['postcode'])
# Remove the quotes and split the strings, which results in a Series made up of 2-element lists
postcodes = df['postcode'].str.replace("'", "").str.split(';')
# Unpack the transposed postcodes into 2 new columns
df['postcode1'], df['postcode2'] = zip(*postcodes)
# Delete the original column
del df['postcode']
print(df)
Output:
postcode1 postcode2
0 FRH02 29290
1 FRH01 29300
2 FRT02 29310
3 FRH03 29340
4 FRH05 29350
5 FRG02 29360

You can use Series.str.split:
p1 = []
p2 = []
for row in df['postcode'].str.split(';'):
p1.append(row[0])
p2.append(row[1])
df2 = pd.DataFrame()
df2["postcode1"] = p1
df2["postcode2"] = p2

How to group data by count of columns in Pandas?

I have a CSV file with a lot of rows and different number of columns.
How to group data by count of columns and show it in different frames?
File CSV has the following data:
1 OLEG US FRANCE BIG
1 OLEG FR 18
1 NATA 18
Because I have different number of colums in each row I have to group rows by count of columns and show 3 frames to be able set header then:
ID NAME STATE COUNTRY HOBBY
FR1: 1 OLEG US FRANCE BIG
ID NAME COUNTRY AGE
FR2: 1 OLEG FR 18
FR3:
ID NAME AGE
1 NATA 18
Any words, I need to group rows by count of columns and show them in different dataframes.

since pandas doesn't allow you to have different length of columns, just don't use it to import your data. Your goal is to create three seperate df, so first import the data as lists, and then deal with it and its differents lengths.
One way to solve this is read the data with csv.reader and create the df's with list comprehension together with a condition for the length of the lists.
with open('input.csv', 'r') as f:
reader = csv.reader(f, delimiter=' ')
data= list(reader)
df1 = pd.DataFrame([item for item in data if len(item)==3], columns='ID NAME AGE'.split())
df2 = pd.DataFrame([item for item in data if len(item)==4], columns='ID NAME COUNTRY AGE'.split())
df3 = pd.DataFrame([item for item in data if len(item)==5], columns='ID NAME STATE COUNTRY HOBBY'.split())
print(df1, df2, df3, sep='\n\n')
ID NAME AGE
0 1 NATA 18
ID NAME COUNTRY AGE
0 1 OLEG FR 18
ID NAME STATE COUNTRY HOBBY
0 1 OLEG US FRANCE BIG
If you need to hardcode too many lines for the same step (e.g. too many df's), then you should consider using a loop to create them and store each dataframe as key/value in a dictionary.
EDIT
Here is the little optimizedway of creating those df's. I think you can't get around creating a list of columns you want to use for the seperate df's, so you need to know what variations of number of columns you have in your data (except you want to create those df's without naming the columns.
col_list=[['ID', 'NAME', 'AGE'],['ID', 'NAME', 'COUNTRY', 'AGE'],['ID', 'NAME', 'STATE', 'COUNTRY', 'HOBBY']]
with open('input.csv', 'r') as f:
reader = csv.reader(f, delimiter=' ')
data= list(reader)
dict_of_dfs = {}
for cols in col_list:
dict_of_dfs[f'df_{len(cols)}'] = pd.DataFrame([item for item in data if len(item)==len(cols)], columns=cols)
for key,val in dict_of_dfs.items():
print(f'{key=}: \n {val} \n')
key='df_3':
ID NAME AGE
0 1 NATA 18
key='df_4':
ID NAME COUNTRY AGE
0 1 OLEG FR 18
key='df_5':
ID NAME STATE COUNTRY HOBBY
0 1 OLEG US FRANCE BIG
Now you don't have variables for your df, instead you have them in a dictionary as keys. (I named the df with the number of columns it has, df_3 is the df with three columns.
If you need to import the data with pandas, you could have a look at this post.

How to read a file containing more than one record type within?

I have a .csv file that contains 3 types of records, each with different quantity of columns.
I know the structure of each record type and that the rows are always of type1 first, then type2 and type 3 at the end, but I don't know how many rows of each record type there are.
The first 4 characters of each row define the record type of that row.
CSV Example:
typ1,John,Smith,40,M,Single
typ1,Harry,Potter,22,M,Married
typ1,Eva,Adams,35,F,Single
typ2,2020,08,16,A
typ2,2020,09,02,A
typ3,Chevrolet,FC101TT,2017
typ3,Toyota,CE972SY,2004
How can I read It with Pandas? It doesn't matter if I have to read one record type each time.
Thanks!!

Here it is a pandas solution.
First we must read the csv file in a way that pandas keeps the entires lines in one cell each. We do that by simply using a wrong separator, such as the 'at' symbol '#'. It can be whatever we want, since we guarantee it won't ever appear in our data file.
wrong_sep = '#'
right_sep = ','
df = pd.read_csv('my_file.csv', sep=wrong_sep).iloc[:, 0]
The .iloc[:, 0] is used as a quick way to convert a DataFrame into a Series.
Then we use a loop to select the rows that belong to each data structure based on their starting characters. Now we use the "right separator" (probably a comma ',') to split the desired data into real DataFrames.
starters = ['typ1', 'typ2', 'typ3']
detected_dfs = dict()
for start in starters:
_df = df[df.str.startswith(start)].str.split(right_sep, expand=True)
detected_dfs[start] = _df
And here you go. If we print the resulting DataFrames, we get:
0 1 2 3 4 5
0 typ1 Harry Potter 22 M Married
1 typ1 Eva Adams 35 F Single
0 1 2 3 4
2 typ2 2020 08 16 A
3 typ2 2020 09 02 A
0 1 2 3
4 typ3 Chevrolet FC101TT 2017
5 typ3 Toyota CE972SY 2004
Let me know if it helped you!

Not Pandas:
from collections import defaultdict
filename2 = 'Types.txt'
with open(filename2) as dataLines:
nL = dataLines.read().splitlines()
defDList = defaultdict(list)
subs = ['typ1','typ2','typ3']
dataReadLines = [defDList[i].append(j) for i in subs for j in nL if i in j]
# dataReadLines = [i for i in nL]
print(defDList)
Output:
defaultdict(<class 'list'>, {'typ1': ['typ1,John,Smith,40,M,Single', 'typ1,Harry,Potter,22,M,Married', 'typ1,Eva,Adams,35,F,Single'], 'typ2': ['typ2,2020,08,16,A', 'typ2,2020,09,02,A'], 'typ3': ['typ3,Chevrolet,FC101TT,2017', 'typ3,Toyota,CE972SY,2004']})

You can make use of the skiprows parameter of pandas read_csv method to skip the rows you are not interested in for a particular record type. The following gives you a dictionary dfs of dataframes for each type. An advantage is that records of the same types don't necessarily have to be adjacent to each other in the csv file.
For larger files you might want to adjust the code such that the file is only read once instead of twice.
import pandas as pd
from collections import defaultdict
indices = defaultdict(list)
types = ['typ1', 'typ2', 'typ3']
filename = 'test.csv'
with open(filename) as csv:
for idx, line in enumerate(csv.readlines()):
for typ in types:
if line.startswith(typ):
indices[typ].append(idx)
dfs = {typ: pd.read_csv(filename, header=None,
skiprows=lambda x: x not in indices[typ])
for typ in types}

Read the file as a CSV file using the CSV reader. The reader fortunately does not care about line formats:
import csv
with open("yourfile.csv") as infile:
data = list(csv.reader(infile))
Collect the rows with the same first element and build a dataframe of them:
import pandas as pd
from itertools import groupby
dfs = [pd.DataFrame(v) for _,v in groupby(data, lambda x: x[0])]
You've got a list of three dataframes (or as many as necessary).
dfs[1]
# 0 1 2 3 4
#0 typ2 2020 08 16 A
#1 typ2 2020 09 02 A

Parsing a JSON string enclosed with quotation marks from a CSV using Pandas

Similar to this question, but my CSV has a slightly different format. Here is an example:
id,employee,details,createdAt
1,John,"{"Country":"USA","Salary":5000,"Review":null}","2018-09-01"
2,Sarah,"{"Country":"Australia", "Salary":6000,"Review":"Hardworking"}","2018-09-05"
I think the double quotation mark in the beginning of the JSON column might have caused some errors. Using df = pandas.read_csv('file.csv'), this is the dataframe that I got:
id employee details createdAt Unnamed: 1 Unnamed: 2
1 John {Country":"USA" Salary:5000 Review:null}" 2018-09-01
2 Sarah {Country":"Australia" Salary:6000 Review:"Hardworking"}" 2018-09-05
My desired output:
id employee details createdAt
1 John {"Country":"USA","Salary":5000,"Review":null} 2018-09-01
2 Sarah {"Country":"Australia","Salary":6000,"Review":"Hardworking"} 2018-09-05
I've tried adding quotechar='"' as the parameter and it still doesn't give me the result that I want. Is there a way to tell pandas to ignore the first and the last quotation mark surrounding the json value?

As an alternative approach you could read the file in manually, parse each row correctly and use the resulting data to contruct the dataframe. This works by splitting the row both forward and backwards to get the non-problematic columns and then taking the remaining part:
import pandas as pd
data = []
with open("e1.csv") as f_input:
for row in f_input:
row = row.strip()
split = row.split(',', 2)
rsplit = [cell.strip('"') for cell in split[-1].rsplit(',', 1)]
data.append(split[0:2] + rsplit)
df = pd.DataFrame(data[1:], columns=data[0])
print(df)
This would display your data as:
id employee details createdAt
0 1 John {"Country":"USA","Salary":5000,"Review":null} 2018-09-01
1 2 Sarah {"Country":"Australia", "Salary":6000,"Review"... 2018-09-05

I have reproduced your file
With
df = pd.read_csv('e1.csv', index_col=None )
print (df)
Output
id emp details createdat
0 1 john "{"Country":"USA","Salary":5000,"Review":null}" "2018-09-01"
1 2 sarah "{"Country":"Australia", "Salary":6000,"Review... "2018-09-05"

I think there's a better way by passing a regex to sep=r',"|",|(?<=\d),' and possibly some other combination of parameters. I haven't figured it out totally.
Here is a less than optimal option:
df = pd.read_csv('s083838383.csv', sep='##$%^', engine='python')
header = df.columns[0]
print(df)
Why sep='##$%^' ? This is just garbage that allows you to read the file with no sep character. It could be any random character and is just used as a means to import the data into a df object to work with.
df looks like this:
id,employee,details,createdAt
0 1,John,"{"Country":"USA","Salary":5000,"Review...
1 2,Sarah,"{"Country":"Australia", "Salary":6000...
Then you could use str.extract to apply regex and expand the columns:
result = df[header].str.extract(r'(.+),(.+),("\{.+\}"),(.+)',
expand=True).applymap(str.strip)
result.columns = header.strip().split(',')
print(result)
result is:
id employee details createdAt
0 1 John "{"Country":"USA","Salary":5000,"Review":null}" "2018-09-01"
1 2 Sarah "{"Country":"Australia", "Salary":6000,"Review... "2018-09-05"
If you need the starting and ending quotes stripped off of the details string values, you could do:
result['details'] = result['details'].str.strip('"')
If the details object items needs to be a dicts instead of strings, you could do:
from json import loads
result['details'] = result['details'].apply(loads)