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KEGG Drug database Python script

I have a drug database saved in a SINGLE column in CSV file that I can read with Pandas. The file containts 750000 rows and its elements are devided by "///". The column also ends with "///". Seems every row is ended with ";".
I would like to split it to multiple columns in order to create structured database. Capitalized words (drug information) like "ENTRY", "NAME" etc. will be headers of these new columns.
So it has some structure, although the elements can be described by different number and sort of information. Meaning some elements will just have NaN in some cells. I have never worked with such SQL-like format, it is difficult to reproduce it as Pandas code, too. Please, see the PrtScs for more information.
An example of desired output would look like this:
df = pd.DataFrame({
"ENTRY":["001", "002", "003"],
"NAME":["water", "ibuprofen", "paralen"],
"FORMULA":["H2O","C5H16O85", "C14H24O8"],
"COMPONENT":[NaN, NaN, "paracetamol"]})
I am guessing there will be .split() involved based on CAPITALIZED words? The Python 3 code solution would be appreciated. It can help a lot of people. Thanks!

Whatever he could, he helped:
import pandas as pd
cols = ['ENTRY', 'NAME', 'FORMULA', 'COMPONENT']
# We create an additional dataframe.
dfi = pd.DataFrame()
# We read the file, get two columns and leave only the necessary lines.
df = pd.read_fwf(r'drug', header=None, names=['Key', 'Value'])
df = df[df['Key'].isin(cols)]
# To "flip" the dataframe, we first prepare an additional column
# with indexing by groups from one 'ENTRY' row to another.
dfi['Key1'] = dfi['Key'] = df[(df['Key'] == 'ENTRY')].index
dfi = dfi.set_index('Key1')
df = df.join(dfi, lsuffix='_caller', rsuffix='_other')
df.fillna(method="ffill", inplace=True)
df = df.astype({"Key_other": "Int64"})
# Change the shape of the table.
df = df.pivot(index='Key_other', columns='Key_caller', values='Value')
df = df.reindex(columns=cols)
# We clean up the resulting dataframe a little.
df['ENTRY'] = df['ENTRY'].str.split(r'\s+', expand=True)[0]
df.reset_index(drop=True, inplace=True)
pd.set_option('display.max_columns', 10)
Small code refactoring:
import pandas as pd
cols = ['ENTRY', 'NAME', 'FORMULA', 'COMPONENT']
# We read the file, get two columns and leave only the necessary lines.
df = pd.read_fwf(r'C:\Users\ф\drug\drug', header=None, names=['Key', 'Value'])
df = df[df['Key'].isin(cols)]
# To "flip" the dataframe, we first prepare an additional column
# with indexing by groups from one 'ENTRY' row to another.
df['Key_other'] = None
df.loc[(df['Key'] == 'ENTRY'), 'Key_other'] = df[(df['Key'] == 'ENTRY')].index
df['Key_other'].fillna(method="ffill", inplace=True)
# Change the shape of the table.
df = df.pivot(index='Key_other', columns='Key', values='Value')
df = df.reindex(columns=cols)
# We clean up the resulting dataframe a little.
df['ENTRY'] = df['ENTRY'].str.split(r'\s+', expand=True)[0]
df['NAME'] = df['NAME'].str.split(r'\(', expand=True)[0]
df.reset_index(drop=True, inplace=True)
pd.set_option('display.max_columns', 10)
print(df)
Key ENTRY NAME FORMULA \
0 D00001 Water H2O
1 D00002 Nadide C21H28N7O14P2
2 D00003 Oxygen O2
3 D00004 Carbon dioxide CO2
4 D00005 Flavin adenine dinucleotide C27H33N9O15P2
... ... ... ...
11983 D12452 Fostroxacitabine bralpamide hydrochloride C22H30BrN4O8P. HCl
11984 D12453 Guretolimod C24H34F3N5O4
11985 D12454 Icenticaftor C12H13F6N3O3
11986 D12455 Lirafugratinib C28H24FN7O2
11987 D12456 Lirafugratinib hydrochloride C28H24FN7O2. HCl
Key COMPONENT
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
... ...
11983 NaN
11984 NaN
11985 NaN
11986 NaN
11987 NaN
[11988 rows x 4 columns]
Need a little more to bring to mind, I leave it to your work.

Pandas read_csv for a no quote file

I'm trying to read a file that doesn't have any quotes, which is causing inconsistent number of row lengths
Data looks as follows:
col_a, col_b
abc, inc., 5
xyz corb, 10
Since there are no quotes around "abc, inc.", this is causing the first row to get split into 3 values, but it should actually be just 2 values.
This column is not necessarily in the first position, and that there can be another bad column like this. The data has around 250 columns.
I'm reading this using pd.read_csv, how can this be resolved?
Thanks!

Its not a CSV but since there is only one column with the errant commas you can process with the csv module and fix the slice that holds too many column values. When a row has too many cells, assume they are the ones from the unescaped comma.
import pandas as pd
import csv
def split_badrows(fileobj, bad_col, total_cols):
"""Iterate rows, colapsing extra columns at bad_col"""
for row in csv.reader(fileobj):
row = [cell.strip() for cell in row]
extras = len(row) - total_cols
if extras > 0:
# colapse slice at troubled column into single value
extras += 1 # python slice doesn't include right endpoint
row[bad_col] = ", ".join(row[bad_col:bad_col+extras])
del row[bad_col+1:bad_col+extras]
yield row
def df_from_badtext(fileobj, bad_col):
"""Make pandas.DataFrame from badly formatted text"""
columns = [cell.strip() for cell in next(fileobj).split(",")]
total_cols = len(columns)
return pd.DataFrame(split_badrows(fileobj, bad_col, total_cols),
columns=columns)
# test
open("testme.txt", "w").write("""col_a, col_b
abc, inc., 5
xyz corb, 10""")
df = df_from_badtext(open("testme.txt"), bad_col=0)
print(df)

Data split to list then transform to dataframe.
csv = '''col_a, col_b
abc, inc., 5
xyz corb, 10'''+'\n'
import re
import pandas as pd
reArr = re.findall('(.*),([^,]+)\n',csv)
df=pd.DataFrame(reArr[1:],columns=reArr[0])
print(df)
col_a
col_b
0
abc, inc.
5
1
xyz corb
10

EDIT:
Thanks to tdelaney comment below:
see if this works
pd.read_csv('foo.csv',delimiter=",(?!( [\w\d]*).,)").dropna(axis=1)
OLD:
using delimiter as ",(?!.*,)" in read_csv seems to be solving this for me

EDIT (after updated question with an additional column):
Solution 1:
You can create a function with the bad column as a parameter and use split and concat to correct the dataframe depending on that bad column. Please note that the bad_col parameter in my function is the column number, where we start counting at 1, rather than 0 (e.g. 1, 2, 3, etc. instead of 0, 1, 2, etc.):
import pandas as pd
import numpy as np
from io import StringIO
data = StringIO('''
col, col_a, col_b
000, abc, inc., 5
111, xyz corb, 10
''')
df = pd.read_csv(data, sep="|")
def fix_csv(df, bad_col):
cols = df.columns.str.split(', ')[0]
x = len(cols) - bad_col
tmp = df.iloc[:,0].str.split(', ', expand=True, n=x)
df = pd.concat([tmp.iloc[:,0],
tmp.iloc[:,-1].str.rsplit(', ', expand=True, n=x)],
axis=1)
df.columns = cols
return df
fix_csv(df, bad_col=2)
Solution 2 (this is if you have issues in multiple columns and you need to use more brute force):
It sounds like there is a possibility that you there could be multiple columns affected from the comments as you mentioned only 1 "so far".
As such, this might be a little bit of a project to clean up the data. The following code can give you an idea how to do that. The bottom-line is that you can create two different dataframes: 1) The first dataframe has the minimum number of commas (i.e. they should be the rows without any issues). 2) The other dataframe will be the dataframe with all of the issues. I've shown how you can clean the data to get to the correct number of columns and then change the data back and concat the two dataframes.
import pandas as pd
import numpy as np
from io import StringIO
data = StringIO('''
col, col_a, col_b
000, abc, inc., 5
111, xyz corb, 10
''')
df = pd.read_csv(data, sep="|")
cols = df.columns.str.split(', ')[0]
s = df.iloc[:,0].str.count(',')
df1 = df.copy()[s.eq(s.min())]
df1 = df1.iloc[:,0].str.split(', ', expand=True)
df1.columns = cols
df2 = df.copy()[s.gt(s.min())]
#inspect this dataframe manually to see how many rows affected, which columns, etc.
#cleanup df2 with some .replace so all equal commas
original = [', inc.', ', corp.']
temp = [' inc.', ' corp.']
df2.iloc[:,0] = df2.iloc[:,0].replace(original, temp, regex=True)
df2 = df2.iloc[:,0].str.split(', ', expand=True)
df2.columns = cols
#cleanup df2 by changing back to original values
df2['col_a'] = df2['col_a'].replace(temp, original, regex=True) # you can do this with other columns as well
df3 = pd.concat([df1, df2]).sort_index()
df3
Out[1]:
col col_a col_b
0 000 abc, inc. 5
1 111 xyz corb 10
Solution 3: Previous Solution (for original question when problem was only in first column - for reference)
You can read in with sep="|" as that | character is not in your .csv, so it reads all of the data into one column.
The main assumption to my solution is that the problematic column is only the first column. I use rsplit(', ') and limit the number of splits to the total number of columns minus 1 (with the example data, this is 2-1=1). Hopefully, this solves with your actual data or at least gives you some idea. If your data is separated by , instead of , , please note whether or not to adjust my splits as well.
import pandas as pd
import numpy as np
from io import StringIO
data = StringIO('''
col_a, col_b
abc, inc., 5
xyz corb, 10
''')
df = pd.read_csv(data, sep="|")
cols = df.columns.str.split(', ')[0]
x = len(cols) - 1
df = df.iloc[:,0].str.rsplit(', ', expand=True, n=x)
df.columns = cols
df
Out[1]:
col_a col_b
0 abc, inc. 5
1 xyz corb 10

How to match values of a dataframe with another dataframe in Python? [duplicate]

I am merging two csv(data frame) using below code:
import pandas as pd
a = pd.read_csv(file1,dtype={'student_id': str})
df = pd.read_csv(file2)
c=pd.merge(a,df,on='test_id',how='left')
c.to_csv('test1.csv', index=False)
I have the following CSV files
file1:
test_id, student_id
1, 01990
2, 02300
3, 05555
file2:
test_id, result
1, pass
3, fail
after merge
test_id, student_id , result
1, 1990, pass
2, 2300,
3, 5555, fail
If you notice student_id has 0 appended at the beginning and it's supposed to be considered as text but after merging and using to_csv function it converts it into numeric and removes leading 0.
How can I keep the column as "text" even after to_csv?
I think its to_csv function which saves back again as numeric
Added dtype={'student_id': str} while reading csv.. but while saving it as to_csv .. it again convert it to numeric

Caveat Please use merge or join. This answer is provided to give perspective on the flexibility pandas gives you and how many different ways there are to answer the same question.
a = pd.read_csv('file1.csv', converters=dict(student_id=str), skipinitialspace=True)
df = pd.read_csv('file2.csv')
results = pd.concat(
[d.set_index('test_id') for d in [a, df]],
axis=1, join='outer'
).reset_index()

It's not dropping the leading zero on the merge, it's dropping it on the read_csv. You can fix this by specifying that column is a string at import time:
a = pd.read_csv('file1.csv', dtype={'student_id': str}, skipinitialspace=True)
The important part is the dtype parameter. You are telling pandas to import this column as a string. The skipinitialspace parameter is set to True, because the column headers are defined with spaces, so we strip it:
test_id, student_id
^ The student_id starts here, at the space
The final code looks like this:
a = pd.read_csv('file1.csv', dtype={'student_id': str}, skipinitialspace=True)
df = pd.read_csv('file2.csv')
results = a.merge(df, how='left', on='test_id')
With the results dataframe looking like this:
test_id student_id result
0 1 01990 pass
1 2 02300 NaN
2 3 05555 fail
Then when you run to_csv your result should be:
test_id,student_id, result
1,01990, pass
2,02300,
3,05555, fail

Solution with join, first need read_csv with parameter dtype for convert student_id to string and remove whitespaces by skipinitialspace:
df1 = pd.read_csv(file1, dtype={'student_id': str}, skipinitialspace=True)
df2 = pd.read_csv(file2, skipinitialspace=True)
df = df1.join(df2.set_index('test_id'), on='test_id')
print (df)
test_id student_id result
0 1 01990 pass
1 2 02300 NaN
2 3 05555 fail

a = pd.read_csv(file1, dtype={'test_id': object})
df = pd.read_csv(file2, dtype={'test_id': object})
==============================================================
In[28]: pd.merge(a, b, on='test_id', how='left')
Out[28]:
test_id student_id result
0 01 1990 pass
1 02 2300 NaN
2 003 5555 fail

Python: store a value in a variable so that you can recognize each reoccurence

If this question is unclear, I am very open to constructive criticism.
I have an excel table with about 50 rows of data, with the first column in each row being a date. I need to access all the data for only one date, and that date appears only about 1-5 times. It is the most recent date so I've already organized the table by date with the most recent being at the top.
So my goal is to store that date in a variable and then have Python look only for that variable (that date) and take only the columns corresponding to that variable. I need to use this code on 100's of other excel files as well, so it would need to arbitrarily take the most recent date (always at the top though).
My current code below simply takes the first 5 rows because I know that's how many times this date occurs.
import os
from numpy import genfromtxt
import pandas as pd
path = 'Z:\\folderwithcsvfile'
for filename in os.listdir(path):
file_path = os.path.join(path, filename)
if os.path.isfile(file_path):
broken_df = pd.read_csv(file_path)
df3 = broken_df['DATE']
df4 = broken_df['TRADE ID']
df5 = broken_df['AVAILABLE STOCK']
df6 = broken_df['AMOUNT']
df7 = broken_df['SALE PRICE']
print (df3)
#print (df3.head(6))
print (df4.head(6))
print (df5.head(6))
print (df6.head(6))
print (df7.head(6))

This is a relatively simple filtering operation. You state that you want to "take only the columns" that are the latest date, so I assume that an acceptable result will be a filter DataFrame with just the correct columns.
Here's a simple CSV that is similar to your structure:
DATE,TRADE ID,AVAILABLE STOCK
10/11/2016,123,123
10/11/2016,123,123
10/10/2016,123,123
10/9/2016,123,123
10/11/2016,123,123
Note that I mixed up the dates a little bit, because it's hacky and error-prone to just assume that the latest dates will be on the top. The following script will filter it appropriately:
import pandas as pd
import numpy as np
df = pd.read_csv('data.csv')
# convert the DATE column to datetimes
df['DATE'] = pd.to_datetime(df['DATE'])
# find the latest datetime
latest_date = df['DATE'].max()
# use index filtering to only choose the columns that equal the latest date
latest_rows = df[df['DATE'] == latest_date]
print (latest_rows)
# now you can perform your operations on latest_rows
In my example, this will print:
DATE TRADE ID AVAILABLE STOCK
0 2016-10-11 123 123
1 2016-10-11 123 123
4 2016-10-11 123 123

Upsample data and interpolate

I have the following dataframe:
Month Col_1 Col_2
1 0,121 0,123
2 0,231 0,356
3 0,150 0,156
4 0,264 0,426
...
I need to resample this to weekly resolution and to interpolate between the points. The latter part, the interpolation is straight-forward. The reindex part is a bit tricky, on the other hand, at least for me.
If I use the DataFrame.reindex() method, it will only erase all the entries from the dataframe. I have tried to do it manually, by using .loc() to create new 'NaN' entries between each consecutive months, but this method overwrites the entries I already have.
Any clue how to do it? Thanks!

I have to assume a start date, I chose 2009-12-31.
To get resample to work, you need a pd.DateTimeIndex.
start_date = pd.to_datetime('2009-12-31')
df.Month = df.Month.apply(lambda x: start_date + pd.offsets.MonthEnd(x))
df = df.set_index('Month')
df.resample('W').interpolate()
Replicable code
from StringIO import StringIO
import pandas as pd
text = """Month Col_1 Col_2
1 0,121 0,123
2 0,231 0,356
3 0,150 0,156
4 0,264 0,426"""
df = pd.read_csv(StringIO(text), decimal=',', delim_whitespace=True)
start_date = pd.to_datetime('2009-12-31')
df.Month = df.Month.apply(lambda x: start_date + pd.offsets.MonthEnd(x))
df = df.set_index('Month')
df.resample('W').interpolate()