I am going to provide you one code. Please check and fix this.

import pandas as pd

import numpy as np

from datetime import datetime

def convert$\_$to$\_$int$($value$)$:

return int$($value$)$

def replace$\_$negatives$\_$with$\_$averages$($df$)$:

rows, cols $=$ df$.$shape

for row in range$($rows$)$:

row$\_$values $=[]$

for col in range$($cols$)$:

value $=$ df$[$col$][$row$]$

if isinstance$($value$,($int$))$ and value $!=-1$:

row$\_$values.append$($value$)$

for col in range$($cols$)$:

if df$[$col$][$row$]==-1$:

next$\_$value $=7$

valid$\_$values $=[]$

new$\_$col $=$ col $+$ next$\_$value

while new$\_$col $<$ cols:

value $=$ df$[$new$\_$col$][$row$]$

if isinstance$($value$,($int$,$ float$))$ and value $!=-1$:

valid$\_$values.append$($value$)$

new$\_$col $=$ new$\_$col $+$ next$\_$value

avg $=$ sum$($valid$\_$values$)/$ len$($valid$\_$values$)$

for col$\_1$ in range$($col$,$ cols, next$\_$value$)$:

if df$[$col$\_1][$row$]==-1$:

df$[$col$\_1][$row$]=$ avg

def process$\_$csv$($file$\_$path, output$\_$path$=$'output.csv$\text{'})$:

processed$\_$data $=\{\}$

df $=$ pd$.$read$\_$csv$($file$\_$path, header$=$None$)$

data $=$ df$.$copy$()$

for col in data.columns:

processed$\_$column $=$ data$[$col$][1$:$]$

processed$\_1=$pd$.$DataFrame$($processed$\_$column$)$

date $=$ str$($processed$\_$column$[1])$

if date $=="---"$:

processed$\_$column$[1]=-2$

# blank$\_$date $=$ processed$\_$column.iloc$[0]$

processed$\_$column$[2$:$]=-1$

processed$\_$data$[$col$]=$ processed$\_$column.tolist$()$

else:

day$\_$index $=$ datetime.strptime$($date$,\text{'}\%$d$/\%$m$/\%$Y$\text{'}).$weekday$()$

processed$\_$column$[1]=$ day$\_$index

processed$\_$data$[$col$]=$ processed$\_$column.tolist$()$

processed$\_$df $=$ pd$.$DataFrame$($processed$\_$data$)$

processed$\_$df$.$fillna$(-1,$ inplace$=$True$)$

processed$\_$df $=$ processed$\_$df$.$map$($convert$\_$to$\_$int$)$

pd$.$set$\_$option$(\text{'}$display$.$max$\_$columns', None$)$

processed$\_$df$.$to$\_$csv$(\text{'}$first$-$pre.csv$\text{'},$ index$=$False, header$=$False$)$

new$\_$df $=$ processed$\_$df$.$transpose$()$

# data$\_$dict$=[]$

for idx in range$($len$($new$\_$df$))$:

temp$=$new$\_$df$[0][$idx$]$

if temp $==-2$:

prec $=$ new$\_$df$[0][$idx $-1]$

if prec $<6$:

missing$\_$days $=$ list$($range$($prec $+1,7))$

for i in missing$\_$days:

new$\_$rows $=$ pd$.$DataFrame$(\{0$: i$\},$ index$=[0])$

# Split the DataFrame into two parts

part$\_$before $=$ new$\_$df$[$:idx$]$

part$\_$after $=$ new$\_$df$[$idx:$]$

new$\_$df $=$ pd$.$concat$([$part$\_$before, new$\_$rows, part$\_$after$]).$reset$\_$index$($drop$=$True$)$

# Move the index forward to account for the new row

idx $=$ idx $+1$

next $=$ new$\_$df$[0][$idx $+1]$

if next $>0$: # Fill days from week start until next

missing$\_$days$\_1=$ list$($range$(0,$ next$))$

for i in missing$\_$days$\_1$:

# Split the DataFrame into two parts: before and after the current position

part$\_$before $=$ new$\_$df$[$:idx $+1]$

part$\_$after $=$ new$\_$df$[$idx $+1$:$]$

# Create a new DataFrame with the missing day

new$\_$row $=$ pd$.$DataFrame$(\{0$: i$\},$ index$=[0])$

new$\_$df $=$ pd$.$concat$([$part$\_$before, new$\_$row, part$\_$after$]).$reset$\_$index$($drop$=$True$)$

# Move the index forward to account for the new row

idx $=$idx $+1$

idx $=$ idx $+1$

for i in range$($len$($new$\_$df$))$:

last$\_$value $=$ new$\_$df$[0][$len$($new$\_$df$)-1]$

for i in range$($last$\_$value $+1,7)$:

new$\_$row $=$ pd$.$DataFrame$(\{0$: i$\},$index$=[0])$

new$\_$df $=$ pd$.$concat$([$new$\_$df$,$ new$\_$row$]).$reset$\_$index$($drop$=$True$)$

print$($new$\_$df$)$

# new$\_$df$.$to$\_$csv$(\text{'}$second$\_$pre.csv$\text{'})$

new$\_$df $=$ pd$.$DataFrame$($new$\_$df$).$transpose$()$

new$\_$df$.$fillna$(-1,$ inplace$=$True$)$

new$\_$df $=$ new$\_$df$.$map$($convert$\_$to$\_$int$)$

new$\_$df$.$to$\_$csv$(\text{'}$final$.$csv$\text{'})$

if $($new$\_$df$[0]==-2).$any$()$:

new$\_$df $=$ new$\_$df$.$drop$($columns$=[-2])$

week$\_$labels $=[]$

week$\_$counter $=1$

for i$,$ col in enumerate$($new$\_$df$.$columns$)$:

day $=$ new$\_$df$[$col$][0]$

if i $!=0$ and day $==0$:

week$\_$counter $+=1$

week$\_$label $=$ f'week$\_\{$week$\_$counter$\}\text{'}$

week$\_$labels.append$($week$\_$label$)$

week$\_$labels$\_$df $=$ pd$.$DataFrame$([$week$\_$labels$],$ columns$=$new$\_$df$.$columns$)$

combined$\_$df $=$ pd$.$concat$([$week$\_$labels$\_$df$,$ new$\_$df$])$

combined$\_$df$.$reset$\_$index$($drop$=$True, inplace$=$True$)$

# for row in combined$\_$df$[1$:$]$:

# combined$\_$df$[$row$]=$ combined$\_$df$[$row$].$apply$($convert$\_$to$\_$int$)$

replace$\_$negatives$\_$with$\_$averages$($combined$\_$df$)$

final$\_$df $=$ combined$\_$df

print$("$final$\_$df is:$")$

final$\_$df$[1$:$]=$ final$\_$df$[1$:$].$astype$($int$)$

print$("$final df$[1]$ is:$",$ final$\_$df$[1$:$])$

exit$(0)$

final$\_$df$.$to$\_$csv$(\text{'}$output$.$csv$\text{'},$ index$=$None, header$=$ None$)$

if $\_\_$name$\_\_==\text{'}\_\_$main$\_\_\text{'}$:

input$\_$file $=$ 'centova$\_$dir$\_$stadio.csv$\text{'}$ # Input file path

output$\_$path $=$ 'output.csv$\text{'}$ # Output file path

result $=$ process$\_$csv$($input$\_$file, output$\_$path$)$

print$("$Processed DataFrame:"$)$

print$($result$)$

Now if you understand the logic you will know that I am trying to replace the negatives $(-1)$ with the average o same hours for same days in weekday values $($ ex$,$ take all monday $7$ pm data and make the average and if there is $-1$ in any monday $7$ pm$,$ replace the $-1$ with the average$).$

in the replace negatives with average function, I want to create two empty matrix of sum and count and then I am going to provide you one pseudo code. Can you please adjust the code by using this?

First create these two matrixes..

then,

for row in $(2,26)$:

for col in cols:

if df$[$col$][$row$]!=-1$ then

sum$[$col$\%7][$row$]<---$sum$[$col$\%7][$row$]+$df$[$col$][$row$]$

count$[$col$\%7][$row$]++$

for row

for col

if df$[$col$][$row$]==1$ then

df$[$col$][$row$]<----$sum$[$col