Could you help to check the error? Because when i run the code, it tells me :ValueError: could not convert string $\text{'}2023-01-01\text{'}$ to float$64$ the data type isDate,Location,Max Temperature $($C$),$Min Temperature $($C$),$Precipitation $($mm$),$Wind Speed $($km$/$h$),$Humidity $(\%),$Cloud Cover $(\%)$

$2023-01-01,$New York,$5,-2,10,20,70,40$

$2023-01-02,$New York,$4,-3,8,18,75,30$

$2023-01-03,$New York,$8,1,5,15,65,20$

$2023-01-04,$New York,$7,0,3,12,80,45$

$2023-01-05,$New York,$6,-1,2,10,85,50$

$2023-01-06,$New York,$3,-4,12,22,78,35$

$2023-01-07,$New York,$2,-5,15,25,82,30$

$2023-01-08,$New York,$1,-6,10,18,88,40$

$2023-01-09,$New York,$4,-3,8,20,85,38$

$2023-01-10,$New York,$6,0,5,15,75,42$

$2023-01-11,$New York,$7,1,2,14,70,38$

$2023-01-12,$New York,$5,-2,4,18,72,36$

$2023-01-13,$New York,$3,-4,6,16,78,40$

$2023-01-14,$New York,$2,-5,8,20,80,45$

$2023-01-15,$New York,$4,-3,10,22,75,35$

$2023-01-16,$New York,$6,0,12,18,68,30$

$2023-01-17,$New York,$7,1,15,15,62,28$

$2023-01-18,$New York,$5,-2,10,20,70,40$

$2023-01-19,$New York,$4,-3,8,18,75,30$

$2023-01-20,$New York,$8,1,5,15,65,20$

$2023-01-21,$New York,$7,0,3,12,80,45$

$2023-01-22,$New York,$6,-1,2,10,85,50$

$2023-01-23,$New York,$3,-4,12,22,78,35$

$2023-01-24,$New York,$2,-5,15,25,82,30,$ so I don't know how to duw with it to filter by city. My current code is #Group Project

#Task $1$

import numpy as np

data $=$ np$.$loadtxt$(\text{'}$data$\_$temperature.txt$\text{'},$ delimiter$=\text{'},\text{'},$ usecols$=[0,1,2,3,4,5,6,7],$ skiprows$=1)$

#Task $2$

def filter$\_$by$\_$city$($data$,$ city$\_$name$)$:

return data$[$data$[$:$,1]==$ city$\_$name.encode$()]$

New$\_$York$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'New York'$)$

Los$\_$Angeles$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'Los Angeles'$)$

London$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'London'$)$

Tokyo$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'Tokyo'$)$

Beijing$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'Beijing'$)$

Sydney$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'Sydney'$)$

Paris$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'Paris'$)$

Berlin$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'Berlin'$)$

Cairo$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'Cairo'$)$

New$\_$Delhi$\_$data $=$ filter$\_$by$\_$city$($data$,$ 'New Delhi'$)$

def calculate$\_$average$($city$\_$data$)$:

max$\_$temps $=$ city$\_$data$[$:$,2].$astype$($float$)$

min$\_$temps $=$ city$\_$data$[$:$,3].$astype$($float$)$

average$\_$temp $=($max$\_$temps $+$ min$\_$temps$)/2$

return average$\_$temp.mean$()$

#New$\_$York

print$($calculate$\_$average$($New$\_$York$\_$data$))$

#Los$\_$Angeles

print$($calculate$\_$average$($Los$\_$Angeles$\_$data$))$

#London

print$($calculate$\_$average$($London$\_$data$))$

#Tokyo

print$($calculate$\_$average$($Tokyo$\_$data$))$

#Beijing

print$($calculate$\_$average$($Beijing$\_$data$))$

#Sydney

print$($calculate$\_$average$($Sydney$\_$data$))$

#Paris

print$($calculate$\_$average$($Paris$\_$data$))$

#Berlin

print$($calculate$\_$average$($Berlin$\_$data$))$

#Cairo

print$($calculate$\_$average$($Cairo$\_$data$))$

#New$\_$Delhi

print$($calculate$\_$average$($New$\_$Delhi$\_$data$))$

#Total$\_$Precipitation

def total$\_$precipitation$($city$\_$data$)$:

total$\_$precipitation $=$ city$\_$data$[$:$,4].$astype$($float$).$sum$()$

return total$\_$precipitation

#New$\_$York

print$($total$\_$precipitation$($New$\_$York$\_$data$))$

#Los$\_$Angeles

print$($total$\_$precipitation$($Los$\_$Angeles$\_$data$))$

#London

print$($total$\_$precipitation$($London$\_$data$))$

#Tokyo

print$($total$\_$precipitation$($Tokyo$\_$data$))$

#Beijing

print$($total$\_$precipitation$($Beijing$\_$data$))$

#Sydney

print$($total$\_$precipitation$($Sydney$\_$data$))$

#Paris

print$($total$\_$precipitation$($Paris$\_$data$))$

#Berlin

print$($total$\_$precipitation$($Berlin$\_$data$))$

#Cairo

print$($total$\_$precipitation$($Cairo$\_$data$))$

#New$\_$Delhi

print$($total$\_$precipitation$($New$\_$Delhi$\_$data$))$

#Wind Speed

def max$\_$wind$\_$speed$($city$\_$data$)$:

return city$\_$data$[$:$,5].$astype$($float$).$max$()$

def min$\_$wind$\_$speed$($city$\_$data$)$:

return city$\_$data$[$:$,5].$astype$($float$).$min$()$

#New$\_$York

print$($max$\_$wind$\_$speed$($New$\_$York$\_$data$))$

print$($min$\_$wind$\_$speed$($New$\_$York$\_$data$))$

#Los$\_$Angeles

print$($max$\_$wind$\_$speed$($Los$\_$Angeles$\_$data$))$

print$($min$\_$wind$\_$speed$($Los$\_$Angeles$\_$data$))$

#London

print$($max$\_$wind$\_$speed$($London$\_$data$))$

print$($min$\_$wind$\_$speed$($Tokyo$\_$data$))$

#Beijing

print$($max$\_$wind$\_$speed$($Beijing$\_$data$))$