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Python- trying to make new list combining values from other list

I'm trying to use two columns from an existing dataframe to generate a list of new strings with those values. I found a lot of examples doing something similar, but not the same thing, so I appreciate advice or links elsewhere if this is a repeat question. Thanks in advance!
If I start with a data frame like this:
import pandas as pd
df=pd.DataFrame(data=[["a",1],["b",2],["c",3]], columns=["id1","id2"])
id1 id2
0 a 1
1 b 2
2 c 3
I want to make a list that looks like
new_ids=['a_1','b_2','c_3'] where values are from combining values in row 0 for id1 with values for row 0 for id2 and so on.
I started by making lists from the columns, but can't figure out how to combine them into a new list. I also tried not using intermediate lists, but couldn't get that either. Error messages below are accurate to the mock data, but are different from the ones with real data.
#making separate lists version
#this function works
def get_ids(orig_df):
id1_list=[]
id2_list=[]
for i in range(len(orig_df)):
id1_list.append(orig_df['id1'].values[i])
id2_list.append(orig_df['id2'].values[i])
return(id1_list,id2_list)
idlist1,idlist2=get_ids(df)
#this is the part that doesn't work
new_id=[]
for i,j in zip(idlist1,idlist2):
row='_'.join(str(idlist1[i]),str(idlist2[j]))
new_id.append(row)
#------------------------------------------------------------------------
#AttributeError Traceback (most recent call #last)
#<ipython-input-44-09983bd890a6> in <module>
# 1 newid_list=[]
# 2 for i in range(len(df)):
#----> 3 n1=df['id1'[i].values]
# 4 n2=df['id2'[i].values]
# 5 nid= str(n1)+"_"+str(n2)
#AttributeError: 'str' object has no attribute 'values'
#skipping making lists (also doesn't work)
newid_list=[]
for i in range(len(df)):
n1=df['id1'[i].values]
n2=df['id2'[i].values]
nid= str(n1)+"_"+str(n2)
newid_list.append(nid)
#---------------------------------------------------------------------------
#TypeError Traceback (most recent call last)
#<ipython-input-41-6b0c949a1ad5> in <module>
# 1 new_id=[]
# 2 for i,j in zip(idlist1,idlist2):
#----> 3 row='_'.join(str(idlist1[i]),str(idlist2[j]))
# 4 new_id.append(row)
# 5 #return ', '.join(new_id)
#TypeError: list indices must be integers or slices, not str

(df.id1 + "_" + df.id2.astype(str)).tolist()
output:
['a_1', 'b_2', 'c_3']
your approaches(corrected):
def get_ids(orig_df):
id1_list=[]
id2_list=[]
for i in range(len(orig_df)):
id1_list.append(orig_df['id1'].values[i])
id2_list.append(orig_df['id2'].values[i])
return(id1_list,id2_list)
idlist1, idlist2=get_ids(df)
#this is the part that doesn't work
new_id=[]
for i,j in zip(idlist1,idlist2):
row='_'.join([str(i),str(j)])
new_id.append(row)
newid_list=[]
for i in range(len(df)):
n1=df['id1'][i]
n2=df['id2'][i]
nid= str(n1)+"_"+str(n2)
newid_list.append(nid)
points:
in first approach, when you loop on data, i and j are data, not indices, so use them as data and convert them to string.
join get list as data and simply define a list using 2 data: [str(i),str(j)] and pass to join
in second approach, you can get every element of every column using df['id1'][i] and you don't need values that return all elements of column as a numpy array
if you want to use values:
(df.id1.values + "_" + df.id2.values.astype(str)).tolist()

Try this:
import pandas as pd
df=pd.DataFrame(data=[["a",1],["b",2],["c",3]], columns=["id1","id2"])
index=0
newid_list=[]
while index < len(df):
newid_list.append(str(df['id1'][index]) + '_' + str(df['id2'][index]))
index+=1

Type error when trying to multiply two Lists in Python

I am trying to write a simple program where a new column is added to an existing dataframe. The new column is created by multiplying values of two existing columns.
This is the code I have written :
import pandas as pd
import numpy as np
data={'Booking Code':['B001','B002','B003','B004','B005'],'Customer Name':['Veer','Umesh','Lavanya','Shobhna','Piyush'],
'No. of Tickets':[4,2,6,5,3], 'Ticket Rate':[100,200,150,250,100],'Booking Clerk':['Manish','Kishor','Manish','John','Kishor']}
df=pd.DataFrame(data)
df=df.to_string(index=False)
print(df)
totalamount=[int(df['No. of Tickets'])* int(df['Ticket Rate']) ]
df['Total Amounts']=totalamount
print(df)
Even though I've used the int() method to convert the values back to integer, it still gives the type error, the exact error being:
Traceback (most recent call last):
File "File Path", line 11, in <module>
totalamount=[int(df['No. of Tickets'])* int(df['Ticket Rate']) ]
TypeError: string indices must be integers
Earlier when I did not have the line df=df.to_string(index=False) line, and also did not use the int() function, there wasn't any error. The list was multiplied, although printed in this manner
[0 400
1 400
2 900
3 1250
4 300
dtype: int64]
But further in the code where I try to add the list to the Dataframe it gives the error ValueError: Length of values (1) does not match length of index (5)
I tried to look any other ways to do this, but can't seem to find any. Thank you in Advance!

you can try this short answer:
df['Total Amounts'] = df.apply(lambda x: x['No. of Tickets'] * x['Ticket Rate'], axis=1)
output:
# print(df['Total Amounts'])
0 400
1 400
2 900
3 1250
4 300
Name: Total Amounts, dtype: int64

You converted your df to a string and again reassigned it to df.
import pandas as pd
import numpy as np
data={'Booking Code':['B001','B002','B003','B004','B005'],'Customer Name':['Veer','Umesh','Lavanya','Shobhna','Piyush'],
'No. of Tickets':[4,2,6,5,3], 'Ticket Rate':[100,200,150,250,100],'Booking Clerk':['Manish','Kishor','Manish','John','Kishor']}
df = pd.DataFrame(data)
string_df = df.to_string(index=False) #Assign it to another variable!
print(string_df)
totalamount = [df['No. of Tickets'] * df['Ticket Rate'] ]
df['Total Amounts'] = totalamount
print(df)

Python error: Invalid literal for int() with base 10

Here is the code and the output. I assume is it about the score not being int, but not sure how to convert in this case
df.index = df.columns
rows = []
for i in df.index:
for c in df.columns:
if i == c: continue
score = df.ix[i, c]
score = [int(row) for row in score.split('-')]
rows.append([i, c, score[0], score[1]])
df = pd.DataFrame(rows, columns = ['home', 'away', 'home_score', 'away_score'])
df.head()

You're splitting on "-" (U+0020 HYPHEN-MINUS), but your data is using some other character... it's hard to say since you provided a picture of the error instead of the actual error, but it's probably "–" (U+2013 EN DASH). Fix your split to use the character that actually occurs in the input.

I think you should just do
score = [int(row) for row in score if row.isnumeric()]
Take advantage of the .isnumeric() method of strings.
P.S. You should not be using .ix method with Pandas. I am pretty sure it is deprecated.

The screenshot you posted is identifying the issue. You are trying to call int() on the str 0-3. This can't be done. From my terminal
In [1]: int('0-3')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-c6bc87cd2bc7> in <module>
----> 1 int('0-3')
ValueError: invalid literal for int() with base 10: '0-3'
Looking at your dataframe it looks like you have a lot of data that can't be turned into an int as is

How to remove every possible accents from a column in python

I am new in python. I have a data frame with a column, named 'Name'. The column contains different type of accents. I am trying to remove those accents. For example, rubén => ruben, zuñiga=zuniga, etc. I wrote following code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
import unicodedata
data=pd.read_csv('transactions.csv')
data.head()
nm=data['Name']
normal = unicodedata.normalize('NFKD', nm).encode('ASCII', 'ignore')
I am getting error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-41-1410866bc2c5> in <module>()
1 nm=data['Name']
----> 2 normal = unicodedata.normalize('NFKD', nm).encode('ASCII', 'ignore')
TypeError: normalize() argument 2 must be unicode, not Series

The reason why it is giving you that error is because normalize requires a string for the second parameter, not a list of strings. I found an example of this online:
unicodedata.normalize('NFKD', u"Durrës Åland Islands").encode('ascii','ignore')
'Durres Aland Islands'

Try this for one column:
nm = nm.str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')
Try this for multiple columns:
obj_cols = data.select_dtypes(include=['O']).columns
data.loc[obj_cols] = data.loc[obj_cols].apply(lambda x: x.str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8'))

Try this for one column:
df[column_name] = df[column_name].apply(lambda x: unicodedata.normalize(u'NFKD', str(x)).encode('ascii', 'ignore').decode('utf-8'))
Change the column name according to your data columns.

array python split str (too many values to unpack)

Array python split str (too many values to unpack)
df.timestamp[1]
Out[191]:
'2016-01-01 00:02:16'
#i need to slept these into to feature
split1,split2=df.timestamp.str.split(' ')
Out[192]:
ValueErrorTraceback (most recent call last)
<ipython-input-216-bbe8e968766f> in <module>()
----> 1 split1,split2=df.timestamp.str.split(' ')
ValueError: too many values to unpack

Use the str[index] since you are splitting the series, the output will also be a series and not two different lists in pandas.
df = pd.DataFrame({'timestamp':['2016-01-01 00:02:16','2016-01-01 00:02:16'] })
split1,split2 = df.timestamp.str.split(' ')[0], df.timestamp.str.split(' ')[1]
str.split will return a series for example
df.timestamp.str.split(' ')
0 [2016-01-01, 00:02:16]
1 [2016-01-01, 00:02:16]
Name: timestamp, dtype: object

You are using split() method wrong.
Given this:
df.timestamp[1]
Out[191]:
'2016-01-01 00:02:16'
Use split() method like this:
# I need to split timestamp[1]
split1, split2 = df.timestamp[1].split(' ') # remove str.