I have the following dataframe:
import pandas as pd
df = pd.DataFrame({'code': ['52511', '52512', '12525', '13333']})
and the following list:
list = ['525', '13333']
I want to consider only the observations of df that start witht the element of list.
Desired output:
import pandas as pd
df = pd.DataFrame({'code': ['52511', '52512', '13333']})
The startswith function supports tuple type. You can convert list to tuple.
listt = ['525', '13333']
df=df[df['code'].str.startswith(tuple(listt))]
df
'''
code
0 52511
1 52512
3 13333
'''
How can I make this account that I made in excel in python...
I wanted to take the column "Acumulado" and multiply by the bottom row of the column 'Selic por diy' and add that value in that row, and so do the same thing successively
import pandas as pd
# Creating the dataframe
df = pd.DataFrame({"Data":['06/03/2006','07/03/2006','08/03/2006','09/03/2006','10/03/2006','13/03/2006','14/03/2006','15/03/2006','16/03/2006','17/03/2006'],
"Taxa SELIC":[17.29,17.29,17.29,16.54,16.54,16.54,16.54,16.54,16.54,16.54,]})
df['Taxa Selic %'] = df['Taxa SELIC'] / 100
df['Selic por dia'] = (1 + df['Taxa SELIC'])**(1/252)
Data frame Example
Here's an example I did in excel
Second example of how I would like it to look
Not an efficient method, but you can try this:
import numpy as np
selic_per_dia = list(df['Selic por dia'].values)
accumulado = [1000000*selic_per_dia[0]]
for i,value in enumerate(selic_per_dia):
if i==0:
continue
else:
accumulado.append(accumulado[i-1]*value)
df['Acumulado'] = accumulado
df.loc[-1] = [np.nan,np.nan,np.nan,np.nan,1000000]
df.index = df.index + 1
df = df.sort_index()
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
I have a df like below :-
import pandas as pd
# intialise data of lists.
data = {'cust':['fnwp', 'utp'], 'events':[['abhi','ashu'],'abhi']}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
df
My expected outcome is :-
You can use pandas.explode() function:
>>> df.explode('events').reset_index(drop=True)
cust events
0 fnwp abhi
1 fnwp ashu
2 utp abhi
I have two data frames, one with historical data and one with some new data appended to the historical data as:
raw_data1 = {'Series_Date':['2017-03-10','2017-03-11','2017-03-12','2017-03-13','2017-03-14','2017-03-15'],'Value':[1,2,3,4,5,6]}
import pandas as pd
df_history = pd.DataFrame(raw_data1, columns = ['Series_Date','Value'])
print df_history
raw_data2 = {'Series_Date':['2017-03-10','2017-03-11','2017-03-12','2017-03-13','2017-03-14','2017-03-15','2017-03-16','2017-03-17'],'Value':[1,2,3,4,4,5,6,7]}
import pandas as pd
df_new = pd.DataFrame(raw_data2, columns = ['Series_Date','Value'])
print df_new
I want to check for all dates in df_history, if data in df_new is different. If data is different then it should append to df_check dataframe as follows:
raw_data3 = {'Series_Date':['2017-03-14','2017-03-15'],'Value_history':[5,6], 'Value_new':[4,5]}
import pandas as pd
df_check = pd.DataFrame(raw_data3, columns = ['Series_Date','Value_history','Value_new'])
print df_check
The key point is that I want to check for all dates that are in my df_history DF and check if a value is present for that day in the df_new DF and if it's same.
Simply run a merge and query filter to capture records where Value_history does not equal Value_new
df_check = pd.merge(df_history, df_new, on='Series_Date', suffixes=['_history', '_new'])\
.query('Value_history != Value_new').reset_index(drop=True)
# Series_Date Value_history Value_new
# 0 2017-03-14 5 4
# 1 2017-03-15 6 5