Develop Reference

Trying to add prefixes to url if not present in pandas df column

I am trying to add prefixes to urls in my 'Websites' Column. I can't figure out how to keep each new iteration of the helper column from overwriting everything from the previous column.
for example say I have the following urls in my column:
http://www.bakkersfinedrycleaning.com/
www.cbgi.org
barstoolsand.com
This would be the desired end state:
http://www.bakkersfinedrycleaning.com/
http://www.cbgi.org
http://www.barstoolsand.com
this is as close as I have been able to get:
def nan_to_zeros(df, col):
new_col = f"nanreplace{col}"
df[new_col] = df[col].fillna('~')
return df
df1 = nan_to_zeros(df1, 'Website')
df1['url_helper'] = df1.loc[~df1['nanreplaceWebsite'].str.startswith('http')| ~df1['nanreplaceWebsite'].str.startswith('www'), 'url_helper'] = 'https://www.'
df1['url_helper'] = df1.loc[df1['nanreplaceWebsite'].str.startswith('http'), 'url_helper'] = ""
df1['url_helper'] = df1.loc[df1['nanreplaceWebsite'].str.startswith('www'),'url_helper'] = 'www'
print(df1[['nanreplaceWebsite',"url_helper"]])
which just gives me a helper column of all www because the last iteration overwrites all fields.
Any direction appreciated.
Data:
{'Website': ['http://www.bakkersfinedrycleaning.com/',
'www.cbgi.org', 'barstoolsand.com']}

IIUC, there are 3 things to fix here:
df1['url_helper'] = shouldn't be there
| should be & in the first condition because 'https://www.' should be added to URLs that start with neither of the strings in the condition. The error will become apparent if we check the first condition after the other two conditions.
The last condition should add "http://" instead of "www".
Alternatively, your problem could be solved using np.select. Pass in the multiple conditions in the conditions list and their corresponding choice list and assign values accordingly:
import numpy as np
s = df1['Website'].fillna('~')
df1['fixed Website'] = np.select([~(s.str.startswith('http') | ~s.str.contains('www')),
~(s.str.startswith('http') | s.str.contains('www'))
],
['http://' + s, 'http://www.' + s], s)
Output:
Website fixed Website
0 http://www.bakkersfinedrycleaning.com/ http://www.bakkersfinedrycleaning.com/
1 www.cbgi.org http://www.cbgi.org
2 barstoolsand.com http://www.barstoolsand.com

How to create new columns of last 5 sale price off in dataframe

I have a pandas data frame of sneakers sale, which looks like this,
I added columns last1, ..., last5 indicating the last 5 sale prices of the sneakers and made them all None. I'm trying to update the values of these new columns using the 'Sale Price' column. This is my attempt to do so,
for index, row in df.iterrows():
if (index==0):
continue
for i in range(index-1, -1, -1):
if df['Sneaker Name'][index] == df['Sneaker Name'][i]:
df['last5'][index] = df['last4'][i]
df['last4'][index] = df['last3'][i]
df['last3'][index] = df['last2'][i]
df['last2'][index] = df['last1'][i]
df['last1'][index] = df['Sale Price'][i]
continue
if (index == 100):
break
When I ran this, I got a warning,
A value is trying to be set on a copy of a slice from a DataFrame
and the result is also wrong.
Does anyone know what I did wrong?
Also, this is the expected output,

Use this instead of for loop, if you have rows sorted:
df['last1'] = df['Sale Price'].shift(1)
df['last2'] = df['last1'].shift(1)
df['last3'] = df['last2'].shift(1)
df['last4'] = df['last3'].shift(1)
df['last5'] = df['last4'].shift(1)

populate column in dataframe with a list using for loop

I would like to populate a dataframe using a for loop.
one of the column is a list.
this list is empty at the begining at each itteration an element is added or removed from it.
when I print my list at each iteration I am getting the right results, but when I print my dataframe, I am getting the same list on each row:
I you have a look to my code the list I am updatin is list_employe. The magic should happen in the 3 last rows but it did not.
Does anyone have an idea why the list is updated in one way and the dataframe record only the last update on all rows
list_employe = []
total_employe = 0
rows=[]
shiftday = example['SHIFT_DATE'].dt.strftime('%Y-%m-%d').unique().tolist()
for i in shiftday:
shift_day = example[example['SHIFT_DATE'] == i]
list_employe_shift = example[example['SHIFT_DATE']==i]['EMPLOYEE_CODE_POS_UPPER'].unique().tolist()
new_employe = 0
end_employe = 0
for k in list_employe_shift:
shift_days_emp = shift_day[shift_day['EMPLOYEE_CODE_POS_UPPER'] == k]
days = shift_days_emp.iloc[0]['last_day']
#print(days)
if k in list_employe:
if days>1:
end_employe= end_employe+1
total_employe = total_employe-1
list_employe.remove(k)
else:
new_employe = new_employe+1
total_employe = total_employe + 1
list_employe.extend([k])
day = i
total_emp = total_employe
new_emp = new_employe
end_emp = end_employe
rows.append([day, total_emp, new_emp, end_emp, list_employe])
print(list_employe)
df = pd.DataFrame(rows, columns=["day", "total_employe", "new_employe", "end_employe", "list_employe"])

the list list_employe is always the same object that you append to the list rows. What you need to do to solve the problem is at the 3rd line from the bottom : rows.append([day, total_emp, new_emp, end_emp, list(list_employe)]) Which create a new list at each itteration

Is it possible to update a row of data using position of column (e.g. like a list index) in Python / SQLAlchemy?

I am trying to compare two rows of data to one another which I have stored in a list.
for x in range(0, len_data_row):
if company_data[0][0][x] == company_data[1][0][x]:
print ('MATCH 1: {} - {}'.format(x, company_data[0][0][x]))
# do nothing
if company_data[0][0][x] == None and company_data[1][0][x] != None:
print ('MATCH 2: {} - {}'.format(x, company_data[1][0][x]))
# update first company_id with data from 2nd
if company_data[0][0][x] != None and company_data[1][0][x] == None:
print ('MATCH 3: {} - {}'.format(x, company_data[0][0][x]))
# update second company_id with data from 1st
Psuedocode of what I want to do:
If data at index[x] of a list is not None for row 2, but is blank for row 1, then write the value of row 2 at index[x] for row 1 data in my database.
The part I can't figure out is if in SQLAlchemy you can do specify which column is being updated by an "index" (I think in db-land index means something different than what I mean. What I mean is like a list index, e.g., list[1]). And also if you can dynamically specify which column is being updated by passing a variable to the update code? Here's what I'm looking to do (it doesn't work of course):
def some_name(column_by_index, column_value):
u = table_name.update().where(table_name.c.id==row_id).values(column_by_index=column_value)
db.execute(u)
Thank you!

Counting the repeated values in one column base on other column

Using Panda, I am dealing with the following CSV data type:
f,f,f,f,f,t,f,f,f,t,f,t,g,f,n,f,f,t,f,f,f,f,f,f,f,f,f,f,f,f,f,f,f,t,t,t,nowin
t,f,f,f,f,f,f,f,f,f,t,f,g,f,b,f,f,t,f,f,f,f,f,t,f,t,f,f,f,f,f,f,f,t,f,n,won
t,f,f,f,t,f,f,f,t,f,t,f,g,f,b,f,f,t,f,f,f,t,f,t,f,t,f,f,f,f,f,f,f,t,f,n,won
f,f,f,f,f,f,f,f,f,f,t,f,g,f,b,f,f,t,f,f,f,f,f,t,f,t,f,f,f,f,f,f,f,t,f,n,nowin
t,f,f,f,t,f,f,f,t,f,t,f,g,f,b,f,f,t,f,f,f,t,f,t,f,t,f,f,f,f,f,f,f,t,f,n,won
f,f,f,f,f,f,f,f,f,f,t,f,g,f,b,f,f,t,f,f,f,f,f,t,f,t,f,f,f,f,f,f,f,t,f,n,win
For this part of the raw data, I was trying to return something like:
Column1_name -- t -- counts of nowin = 0
Column1_name -- t -- count of wins = 3
Column1_name -- f -- count of nowin = 2
Column1_name -- f -- count of win = 1
Based on this idea get dataframe row count based on conditions I was thinking in doing something like this:
print(df[df.target == 'won'].count())
However, this would return always the same number of "wons" based on the last column without taking into consideration if this column it's a "f" or a "t". In other others, I was hoping to use something from Panda dataframe work that would produce the idea of a "group by" from SQL, grouping based on, for example, the 1st and last column.
Should I keep pursing this idea of should I simply start using for loops?
If you need, the rest of my code:
import pandas as pd
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/chess/king-rook-vs-king-pawn/kr-vs-kp.data"
df = pd.read_csv(url,names=[
'bkblk','bknwy','bkon8','bkona','bkspr','bkxbq','bkxcr','bkxwp','blxwp','bxqsq','cntxt','dsopp','dwipd',
'hdchk','katri','mulch','qxmsq','r2ar8','reskd','reskr','rimmx','rkxwp','rxmsq','simpl','skach','skewr',
'skrxp','spcop','stlmt','thrsk','wkcti','wkna8','wknck','wkovl','wkpos','wtoeg','target'
])
features = ['bkblk','bknwy','bkon8','bkona','bkspr','bkxbq','bkxcr','bkxwp','blxwp','bxqsq','cntxt','dsopp','dwipd',
'hdchk','katri','mulch','qxmsq','r2ar8','reskd','reskr','rimmx','rkxwp','rxmsq','simpl','skach','skewr',
'skrxp','spcop','stlmt','thrsk','wkcti','wkna8','wknck','wkovl','wkpos','wtoeg','target']
# number of lines
#tot_of_records = np.size(my_data,0)
#tot_of_records = np.unique(my_data[:,1])
#for item in my_data:
# item[:,0]
num_of_won=0
num_of_nowin=0
for item in df.target:
if item == 'won':
num_of_won = num_of_won + 1
else:
num_of_nowin = num_of_nowin + 1
print(num_of_won)
print(num_of_nowin)
print(df[df.target == 'won'].count())
#print(df[:1])
#print(df.bkblk.to_string(index=False))
#print(df.target.unique())
#ini_entropy = (() + ())

This could work -
outdf = df.apply(lambda x: pd.crosstab(index=df.target,columns=x).to_dict())
Basically we are going in on each feature column and making a crosstab with target column
Hope this helps! :)