Develop Reference

Python remove everything after specific string and loop through all rows in multiple columns in a dataframe

I have a file full of URL paths like below spanning across 4 columns in a dataframe that I am trying to clean:
Path1 = ["https://contentspace.global.xxx.com/teams/Australia/WA/Documents/Forms/AllItems.aspx?\
RootFolder=%2Fteams%2FAustralia%2FWA%2FDocuments%2FIn%20Scope&FolderCTID\
=0x012000EDE8B08D50FC3741A5206CD23377AB75&View=%7B287FFF9E%2DD60C%2D4401%2D9ECD%2DC402524F1D4A%7D"]
I want to remove everything after a specific string which I defined it as "string1" and I would like to loop through all 4 columns in the dataframe defined as "df_MasterData":
string1 = "&FolderCTID"
import pandas as pd
df_MasterData = pd.read_excel(FN_MasterData)
cols = ['Column_A', 'Column_B', 'Column_C', 'Column_D']
for i in cols:
# Objective: Replace "&FolderCTID", delete all string after
string1 = "&FolderCTID"
# Method 1
df_MasterData[i] = df_MasterData[i].str.split(string1).str[0]
# Method 2
df_MasterData[i] = df_MasterData[i].str.split(string1).str[1].str.strip()
# Method 3
df_MasterData[i] = df_MasterData[i].str.split(string1)[:-1]
I did search and google and found similar solutions which were used but none of them work.
Can any guru shed some light on this? Any assistance is appreciated.
Added below is a few example rows in column A and B for these URLs:
Column_A = ['https://contentspace.global.xxx.com/teams/Australia/NSW/Documents/Forms/AllItems.aspx?\
RootFolder=%2Fteams%2FAustralia%2FNSW%2FDocuments%2FIn%20Scope%2FA%20I%20TOPPER%20GROUP&FolderCTID=\
0x01200016BC4CE0C21A6645950C100F37A60ABD&View=%7B64F44840%2D04FE%2D4341%2D9FAC%2D902BB54E7F10%7D',\
'https://contentspace.global.xxx.com/teams/Australia/Victoria/Documents/Forms/AllItems.aspx?RootFolder\
=%2Fteams%2FAustralia%2FVictoria%2FDocuments%2FIn%20Scope&FolderCTID=0x0120006984C27BA03D394D9E2E95FB\
893593F9&View=%7B3276A351%2D18C1%2D4D32%2DADFF%2D54158B504FCC%7D']
Column_B = ['https://contentspace.global.xxx.com/teams/Australia/WA/Documents/Forms/AllItems.aspx?\
RootFolder=%2Fteams%2FAustralia%2FWA%2FDocuments%2FIn%20Scope&FolderCTID=0x012000EDE8B08D50FC3741A5\
206CD23377AB75&View=%7B287FFF9E%2DD60C%2D4401%2D9ECD%2DC402524F1D4A%7D',\
'https://contentspace.global.xxx.com/teams/Australia/QLD/Documents/Forms/AllItems.aspx?RootFolder=%\
2Fteams%2FAustralia%2FQLD%2FDocuments%2FIn%20Scope%2FAACO%20GROUP&FolderCTID=0x012000E689A6C1960E8\
648A90E6EC3BD899B1A&View=%7B6176AC45%2DC34C%2D4F7C%2D9027%2DDAEAD1391BFC%7D']

This is how i would do it,
first declare a variable with your target columns.
Then use stack() and str.split to get your target output.
finally, unstack and reapply the output to your original df.
cols_to_slice = ['ColumnA','ColumnB','ColumnC','ColumnD']
string1 = "&FolderCTID"
df[cols_to_slice].stack().str.split(string1,expand=True)[1].unstack(1)
if you want to replace these columns in your target df then simply do -
df[cols_to_slice] = df[cols_to_slice].stack().str.split(string1,expand=True)[1].unstack(1)

You should first get the index of string using
indexes = len(string1) + df_MasterData[i].str.find(string1)
# This selected the final location of this string
# if you don't want to add string in result just use below one
indexes = len(string1) + df_MasterData[i].str.find(string1)
Now do
df_MasterData[i] = df_MasterData[i].str[:indexes]

How to replace a loop that looks at multiple previous values with a formula in Python

My Problem
I have a loop that creates a column using either a formula based on values from other columns or the previous value in the column depending on a condition ("days from new low == 0"). It is really slow over a huge dataset so I wanted to get rid of the loop and find a formula that is faster.
Current Working Code
import numpy as np
import pandas as pd
csv1 = pd.read_csv('stock_price.csv', delimiter = ',')
df = pd.DataFrame(csv1)
for x in range(1,len(df.index)):
if df["days from new low"].iloc[x] == 0:
df["mB"].iloc[x] = (df["RSI on new low"].iloc[x-1] - df["RSI on new low"].iloc[x]) / -df["days from new low"].iloc[x-1]
else:
df["mB"].iloc[x] = df["mB"].iloc[x-1]
df
Input Data and Expected Output
RSI on new low,days from new low,mB
0,22,0
29.6,0,1.3
29.6,1,1.3
29.6,2,1.3
29.6,3,1.3
29.6,4,1.3
21.7,0,-2.0
21.7,1,-2.0
21.7,2,-2.0
21.7,3,-2.0
21.7,4,-2.0
21.7,5,-2.0
21.7,6,-2.0
21.7,7,-2.0
21.7,8,-2.0
21.7,9,-2.0
25.9,0,0.5
25.9,1,0.5
25.9,2,0.5
23.9,0,-1.0
23.9,1,-1.0
Attempt at Solution
def mB_calc (var1,var2,var3):
df[var3]= np.where(df[var1] == 0, df[var2].shift(1) - df[var2] / -df[var1].shift(1) , "")
return df
df = mB_calc('days from new low','RSI on new low','mB')
First, it gives me this "TypeError: can't multiply sequence by non-int of type 'float'" and second I dont know how to incorporate the "ffill" into the formula.
Any idea how I might be able to do it?
Cheers!

Try this one:
df["mB_temp"] = (df["RSI on new low"].shift() - df["RSI on new low"]) / -df["days from new low"].shift()
df["mB"] = df["mB"].shift()
df["mB"].loc[df["days from new low"] == 0]=df["mB_temp"].loc[df["days from new low"] == 0]
df.drop(["mB_temp"], axis=1)
And with np.where:
df["mB"] = np.where(df["days from new low"]==0, df["RSI on new low"].shift() - df["RSI on new low"]) / -df["days from new low"].shift(), df["mB"].shift())

How to append data to a dataframe whithout overwriting?

I'm new to python but I need it for a personal project. And so I have this lump of code. The function is to create a table and update it as necessary. The problem is that the table keeps being overwritten and I don't know why. Also I'm struggling with correctly assigning the starting position of the new lines to append, and that's why total (ends up overwritten as well) and pos are there, but I haven't figured out how to correctly use them. Any tips?
import datetime
import pandas as pd
import numpy as np
total ={}
entryTable = pd.read_csv("Entry_Table.csv")
newEntries = int(input("How many new entries?\n"))
for i in range(newEntries):
ID = input ("ID?\n")
VQ = int (input ("VQ?\n"))
timeStamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
entryTable.loc[i] = [timeStamp, ID, VQ]
entryTable.to_csv("Inventory_Table.csv")
total[i] = 1
pos = sum(total.values())
print(pos)
inventoryTable = pd.read_csv("Inventory_Table.csv", index_col = 0)

Your variable 'i' runs from index 0 to the number of 'newEntries'. When you add new data to row 'i' in your Pandas dataframe, you are overwriting existing data in that row. If you want to add new data, try 'n+i' where n is the initial number of entries. You can determine n with either
n = len(entryTable)
or
n = entryTable.shape[0]

My program compute values as string and not as float even when ichange the type

i have a problem with my program and i'm confused, i don't know why it won't change the type of the columns, or maybe it is changing the type of the columns and it just still compute the columns as string. When i change the type into float, if i want it to be multiplied by 8, it will give me, for example with 4, 44444444. Here is my code.
import pandas as pd
import re
import numpy as np
link = "excelfilett.txt"
file = open(link, "r")
frames = []
is_count_frames = False
for line in file:
if "[Frames]" in line:
is_count_frames = True
if is_count_frames == True:
frames.append(line)
if "[EthernetRouting]" in line:
break
number_of_rows = len(frames) - 3
header = re.split(r'\t', frames[1])
number_of_columns = len(header)
frame_array = np.full((number_of_rows, number_of_columns), 0)
df_frame_array = pd.DataFrame(frame_array)
df_frame_array.columns= header
for row in range(number_of_rows):
frame_row = re.split(r'\t',frames[row+2])
for position in range(len(frame_row)):
df_frame_array.iloc[row, position]=frame_row[position]
df_frame_array['[MinDistance (ms)]'].astype(float)
df_frame_array.loc[:,'[MinDistance (ms)]'] *= 8
print(df_frame_array['[MinDistance (ms)]'])
but it gives me 8 times the value like (100100...100100), i also tried with puting them in a list
MinDistList = df_frame_array['[MinDistance (ms)]'].tolist()
product = []
for i in MinDistList:
product.append(i*8)
print(product)
but it still won't work, any ideas?

df_frame_array['[MinDistance (ms)]'].astype(float) doesn't change the column in place, but returns a new one.
You had the right idea, so just store it back:
df_frame_array['[MinDistance (ms)]'] = df_frame_array['[MinDistance (ms)]'].astype(float)

Time efficiency by eliminating three for loops

I have the a script similar to this:
import random
import pandas as pd
FA = []
FB = []
Value = []
df = pd.DataFrame()
df_save = pd.DataFrame(index=['min','max'])
days = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
numbers = list(range(24)) # FA.unique()
mix = '(pairwise combination of days and numbers, i.e. 0Monday,0Tuesday,...1Monday,1Tuesday,....)' 'I dont know how to do this combination btw'
def Calculus():
global min,max
min = df['Value'][boolean].min()
max = df['Value'][boolean].max()
for i in range(1000):
FA.append(random.randrange(0,23,1))
FB.append(random.choice(days))
Value.append(random.random())
df['FA'] = FA
df['FB'] = FB
df['FAB'] = df['FA'].astype(str) + df['FB'].astype(str)
df['Value'] = Value
mix_factor = df['FA'].astype(str) + df['FB'].astype(str)
for i in numbers:
boolean = df['FA'] == i
Calculus()
df_save[str(i)] = [min,max]
for i in days:
boolean = df['FB'] == i
Calculus()
df_save[str(i)] = [min,max]
for i in mix_factor.unique():
boolean = df['FAB'] == i
Calculus() #
df_save[str(i)] = [min,max]
My question is: there is another way to do the same but more time efficiently? My real data (df in this case) is a csv with millions of rows and this three loops are taking forever.
Maybe using 'apply' but I never have worked with it before.
Any insight will be very appreciate, thanks.

You could put all three loops into one, depending on what your exact code is. Is there a parameter for calculus? If not, putting them into one would allow you to have to run Calculus() less