We Keep Coding

can't adapt type 'data'

I have data class:
class data:
def __init__(self, ReadTime, Concentration_PM10, Concentration_SO2, Concentration_O3, Concentration_NO2, Concentration_CO, AQI_PM10,
AQI_SO2,AQI_O3, AQI_NO2, AQI_CO, AQI_AQIIndex, AQI_ContaminantParameter, AQI_State, AQI_Color ):
self.ReadTime = ReadTime
self.Concentration_PM10 = Concentration_PM10
self.Concentration_SO2 = Concentration_SO2
self.Concentration_O3 = Concentration_O3
self.Concentration_NO2 = Concentration_NO2
self.Concentration_CO = Concentration_CO
self.AQI_PM10 = AQI_PM10
self.AQI_SO2 = AQI_SO2
self.AQI_O3 = AQI_O3
self.AQI_NO2 = AQI_NO2
self.AQI_CO = AQI_CO
self.AQI_AQIIndex = AQI_AQIIndex
self.AQI_ContaminantParameter = AQI_ContaminantParameter
self.AQI_State = AQI_State
self.AQI_Color = AQI_Color
I'm sending a request to an api and populating the variables into a list.:
list = []
for i in result:
list.append( data(i['ReadTime'], i['Concentration']['PM10'], i['Concentration']['SO2'],i['Concentration']['O3'],
i['Concentration']['NO2'],i['Concentration']['CO'],i['AQI']['PM10'],
i['AQI']['SO2'],i['AQI']['O3'],i['AQI']['NO2'],i['AQI']['CO'],i['AQI']['AQIIndex'],i['AQI']['ContaminantParameter'],
i['AQI']['State'],i['AQI']['Color']))
then I want to insert this list into a table in PostgreSQL but I get error "can't adapt type 'data'"
list_record = ", ".join(["%s"] * len(list))
query_insert= (f"INSERT INTO hava_kalitesi (ReadTime, Concentration_PM10, Concentration_SO2, Concentration_O3, Concentration_NO2, Concentration_CO, AQI_PM10, AQI_SO2, AQI_O3, AQI_NO2, AQI_CO, AQI_AQIIndex, AQI_ContaminantParameter,AQI_State,AQI_Color) VALUES {list_record}"
)
cursor.execute(query_insert,list)

How to get correct output shape in Unet?

I am building a u net for binary image image segmentation. I am using Tensorflow's tf.nn api. My input image has dimensions (256,256,3) and output binary image has dimensions(256,256,1). The output of the U net model must be (1,256,256,1) but output shape results to be (7,256,256,3).For the convolutional kernels I am using Tensorflow's truncated normal initializer with each datatype as float32. Am I creating multiple output layers somewhere in the code
def get_filter(shape,na):
w =tf.get_variable(name=na,shape=shape,dtype='float32',initializer=tf.truncated_normal_initializer(dtype='float32'))
return w
def unet(inp):
#f1 = get_filter(shape=[3,3,3,16])
lay_16_1 = tf.nn.conv2d(inp,filter=get_filter(shape=[3,3,3,16],na='w_1'),strides=[1,1,1,1],padding='SAME',name='conv_16_1')
lay_16_2 = tf.nn.relu(lay_16_1,name='re_16_1')
lay_16_3 = tf.layers.batch_normalization(lay_16_2,axis=-1,name='bn_16')
lay_16_4 = tf.nn.conv2d(lay_16_3,filter=get_filter([3,3,16,16],na='w_2'),strides=[1,1,1,1],padding='SAME',name='conv_16_2')
lay_16_5 = tf.nn.relu(lay_16_4,name='re_16_2')
lay_p1 = tf.nn.max_pool(lay_16_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_1')
lay_32_1 = tf.nn.conv2d(lay_p1,filter=get_filter([3,3,16,32],na='w_3'),strides=[1,1,1,1],padding='SAME',name='conv_32_1')
lay_32_2 = tf.nn.relu(lay_32_1,name='re_32_1')
lay_32_3 = tf.layers.batch_normalization(lay_32_2,axis=-1,name='bn_32')
lay_32_4 = tf.nn.conv2d(lay_32_3,filter=get_filter([3,3,32,32],na='w_4'),strides=[1,1,1,1],padding='SAME',name='conv_32_2')
lay_32_5 = tf.nn.relu(lay_32_4,name='re_32_2')
lay_p2 = tf.nn.max_pool(lay_32_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_2')
lay_64_1 = tf.nn.conv2d(lay_p2,filter=get_filter([3,3,32,64],na='w_5'),strides=[1,1,1,1],padding='SAME',name='conv_64_1')
lay_64_2 = tf.nn.relu(lay_64_1,name='re_64_1')
lay_64_3 = tf.layers.batch_normalization(lay_64_2,axis=-1,name='bn_64')
lay_64_4 = tf.nn.conv2d(lay_64_3,filter=get_filter([3,3,64,64],na='w_6'),strides=[1,1,1,1],padding='SAME',name='conv_64_2')
lay_64_5 = tf.nn.relu(lay_64_4,name='re_64_2')
lay_p3 = tf.nn.max_pool(lay_64_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_3')
lay_128_1 = tf.nn.conv2d(lay_p3,filter=get_filter([3,3,64,128],na='w_7'),strides=[1,1,1,1],padding='SAME',name='conv_128_1')
lay_128_2 = tf.nn.relu(lay_128_1,name='re_128_1')
lay_128_3 = tf.layers.batch_normalization(lay_128_2,axis=-1,name='bn_128')
lay_128_4 = tf.nn.conv2d(lay_128_3,filter=get_filter([3,3,128,128],na='w_8'),strides=[1,1,1,1],padding='SAME',name='conv_128_2')
lay_128_5 = tf.nn.relu(lay_128_4,name='re_128_2')
lay_p4 = tf.nn.max_pool(lay_128_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_4')
lay_256_1 = tf.nn.conv2d(lay_p4,filter=get_filter([3,3,128,256],na='w_9'),strides=[1,1,1,1],padding='SAME',name='conv_256_1')
lay_256_2 = tf.nn.relu(lay_256_1,name='re_256_1')
lay_256_3 = tf.layers.batch_normalization(lay_256_2,axis=-1,name='bn_256')
lay_256_4 = tf.nn.conv2d(lay_256_3,filter=get_filter([3,3,256,256],na='w_10'),strides=[1,1,1,1],padding='SAME',name='conv_256_2')
lay_256_5 = tf.nn.relu(lay_256_4,name='re_256_2')
lay_p5 = tf.nn.max_pool(lay_256_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_5')
lay_512_1 = tf.nn.conv2d(lay_p5,filter=get_filter([3,3,256,512],na='w_11'),strides=[1,1,1,1],padding='SAME',name='conv_512_1')
lay_512_2 = tf.nn.relu(lay_512_1,name='re_512_1')
lay_512_3 = tf.layers.batch_normalization(lay_512_2,axis=-1,name='bn_512')
lay_512_4 = tf.nn.conv2d(lay_512_3,filter=get_filter([3,3,512,512],na='w_12'),strides=[1,1,1,1],padding='SAME',name='conv_512_2')
lay_512_5 = tf.nn.relu(lay_512_4,name='re_512_2')
lay_p6 = tf.nn.max_pool(lay_512_5,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME',name='pool_6')
lay_1024_1 = tf.nn.conv2d(lay_p6,filter=get_filter([3,3,512,1024],na='w_13'),strides=[1,1,1,1],padding='SAME',name='conv_1024_1')
lay_1024_2 = tf.nn.relu(lay_1024_1,name='re_1024_1')
lay_1024_3 = tf.layers.batch_normalization(lay_1024_2,axis=-1,name='bn_1024')
lay_1024_4 = tf.nn.conv2d(lay_1024_3,filter=get_filter([3,3,1024,1024],na='w_14'),strides=[1,1,1,1],padding='SAME',name='conv_1024_2')
lay_1024_5 = tf.nn.relu(lay_1024_4,name='re_1024_2')
#lay_p7 = tf.nn.max_pool(lay_1024,ksize=[1,2,2,1],strides=[1,1,1,1],padding='SAME',name='pool_7')
up_512 = tf.image.resize_images(images=lay_1024_5,size=[8,8],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_512_1 = tf.nn.conv2d(up_512,filter=get_filter([3,3,1024,512],na='w_15'),strides=[1,1,1,1],padding='SAME',name='mer_512_1')
con_512_2 = tf.nn.relu(con_512_1,name='rel_512_1')
mer_512 = tf.concat([lay_512_5,con_512_2],axis=0,name='mer_512_2')
con_512_3 = tf.nn.conv2d(mer_512,filter=get_filter([3,3,512,512],na='w_16'),strides=[1,1,1,1],padding='SAME',name='mer_512_3')
con_512_4 = tf.nn.relu(con_512_3,name='rel_512_2')
con_512_5 = tf.layers.batch_normalization(con_512_4,axis=-1,name='mer_bn_512')
con_512_6 = tf.nn.conv2d(con_512_5,filter=get_filter([3,3,512,512],na='w_17'),strides=[1,1,1,1],padding='SAME',name='mer_512_4')
con_512_7 = tf.nn.relu(con_512_6,name='rel_512_3')
up_256 = tf.image.resize_images(images=con_512_7,size=[16,16],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_256_1 = tf.nn.conv2d(up_256,filter=get_filter([3,3,512,256],na='w_18'),strides=[1,1,1,1],padding='SAME',name='mer_256_1')
con_256_2 = tf.nn.relu(con_256_1,name='rel_256_1')
mer_256 = tf.concat([lay_256_5,con_256_2],axis=0,name='mer_256_2')
con_256_3 = tf.nn.conv2d(mer_256,filter=get_filter([3,3,256,256],na='w_19'),strides=[1,1,1,1],padding='SAME',name='mer_256_3')
con_256_4 = tf.nn.relu(con_256_3,name='rel_256_2')
con_256_5 = tf.layers.batch_normalization(con_256_4,axis=-1,name='mer_bn_256')
con_256_6 = tf.nn.conv2d(con_256_5,filter=get_filter([3,3,256,256],na='w_20'),strides=[1,1,1,1],padding='SAME',name='mer_256_4')
con_256_7 = tf.nn.relu(con_256_6,name='rel_256_3')
up_128 = tf.image.resize_images(images=con_256_7,size=[32,32],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_128_1 = tf.nn.conv2d(up_128,filter=get_filter([3,3,256,128],na='w_21'),strides=[1,1,1,1],padding='SAME',name='mer_128_1')
con_128_2 = tf.nn.relu(con_128_1,name='rel_128_1')
mer_128 = tf.concat([lay_128_5,con_128_2],axis=0,name='mer_128_2')
con_128_3 = tf.nn.conv2d(mer_128,filter=get_filter([3,3,128,128],na='w_22'),strides=[1,1,1,1],padding='SAME',name='mer_128_3')
con_128_4 = tf.nn.relu(con_128_3,name='rel_128_2')
con_128_5 = tf.layers.batch_normalization(con_128_4,axis=-1,name='mer_bn_128')
con_128_6 = tf.nn.conv2d(con_128_5,filter=get_filter([3,3,128,128],na='w_23'),strides=[1,1,1,1],padding='SAME',name='mer_128_4')
con_128_7 = tf.nn.relu(con_128_6,name='rel_128_3')
up_64 = tf.image.resize_images(images=con_128_7,size=[64,64],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_64_1 = tf.nn.conv2d(up_64,filter=get_filter([3,3,128,64],na='w_24'),strides=[1,1,1,1],padding='SAME',name='mer_64_1')
con_64_2 = tf.nn.relu(con_64_1,name='rel_64_1')
mer_64 = tf.concat([lay_64_5,con_64_2],axis=0,name='mer_64_2')
con_64_3 = tf.nn.conv2d(mer_64,filter=get_filter([3,3,64,64],na='w_25'),strides=[1,1,1,1],padding='SAME',name='mer_64_3')
con_64_4 = tf.nn.relu(con_64_3,name='rel_64_2')
con_64_5 = tf.layers.batch_normalization(con_64_4,axis=-1,name='mer_bn_64')
con_64_6 = tf.nn.conv2d(con_64_5,filter=get_filter([3,3,64,64],na='w_26'),strides=[1,1,1,1],padding='SAME',name='mer_64_4')
con_64_7 = tf.nn.relu(con_64_6,name='rel_64_3')
up_32 = tf.image.resize_images(images=con_64_7,size=[128,128],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_32_1 = tf.nn.conv2d(up_32,filter=get_filter([3,3,64,32],na='w_27'),strides=[1,1,1,1],padding='SAME',name='mer_32_1')
con_32_2 = tf.nn.relu(con_32_1,name='rel_32_1')
mer_32 = tf.concat([lay_32_5,con_32_2],axis=0,name='mer_32_2')
con_32_3 = tf.nn.conv2d(mer_32,filter=get_filter([3,3,32,32],na='w_28'),strides=[1,1,1,1],padding='SAME',name='mer_32_3')
con_32_4 = tf.nn.relu(con_32_3,name='rel_32_2')
con_32_5 = tf.layers.batch_normalization(con_32_4,axis=-1,name='mer_bn_32')
con_32_6 = tf.nn.conv2d(con_32_5,filter=get_filter([3,3,32,32],na='w_29'),strides=[1,1,1,1],padding='SAME',name='mer_32_4')
con_32_7 = tf.nn.relu(con_32_6,name='rel_32_3')
up_16 = tf.image.resize_images(images=con_32_7,size=[256,256],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
con_16_1 = tf.nn.conv2d(up_16,filter=get_filter([3,3,32,16],na='w_30'),strides=[1,1,1,1],padding='SAME',name='mer_16_1')
con_16_2 = tf.nn.relu(con_16_1,name='rel_16_1')
mer_16 = tf.concat([lay_16_5,con_16_2],axis=0,name='mer_16_2')
con_16_3 = tf.nn.conv2d(mer_16,filter=get_filter([3,3,16,16],na='w_31'),strides=[1,1,1,1],padding='SAME',name='mer_16_3')
con_16_4 = tf.nn.relu(con_16_3,name='rel_16_2')
con_16_5 = tf.layers.batch_normalization(con_16_4,axis=-1,name='mer_bn_16')
con_16_6 = tf.nn.conv2d(con_16_5,filter=get_filter([3,3,16,16],na='w_32'),strides=[1,1,1,1],padding='SAME',name='mer_16_4')
con_16_7 = tf.nn.relu(con_16_6,name='rel_16_3')
fin_img = tf.nn.conv2d(con_16_7,filter=get_filter([1,1,16,1],na='w_33'),strides=[1,1,1,1],padding='SAME',name='final_image')
#fin_img = tf.nn.sigmoid(fin_img)
return fin_img

Proper way to format date for Fedex API XML

I have a Django application where I am trying to make a call to Fedex's API to send out a shipping label for people wanting to send in a product for cash. When I try to make the call though it says there is a data validation issue with the Expiration field in the XML I am filling out. I swear this has worked in the past with me formatting the date as "YYYY-MM-DD", but now it is not. I read that with Fedex, you need to format the date as ISO, but that is also not passing the data validation. I am using a python package created to help with tapping Fedex's API.
Django view function for sending API Call
def Fedex(request, quote):
label_link = ''
expiration_date = datetime.datetime.now() + datetime.timedelta(days=10)
# formatted_date = "%s-%s-%s" % (expiration_date.year, expiration_date.month, expiration_date.day)
formatted_date = expiration_date.replace(microsecond=0).isoformat()
if quote.device_type != 'laptop':
box_length = 9
box_width = 12
box_height = 3
else:
box_length = 12
box_width = 14
box_height = 3
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
## Page 411 of FedEx Dev Guide - 20.14 Email Labels
CONFIG_OBJ = FedexConfig(key=settings.FEDEX_KEY, password=settings.FEDEX_PASSWORD, account_number=settings.FEDEX_ACCOUNT,
meter_number=settings.FEDEX_METER, use_test_server=settings.USE_FEDEX_TEST)
fxreq = FedexCreatePendingShipRequestEmail(CONFIG_OBJ, customer_transaction_id='xxxxxx id:01')
fxreq.RequestedShipment.ServiceType = 'FEDEX_GROUND'
fxreq.RequestedShipment.PackagingType = 'YOUR_PACKAGING'
fxreq.RequestedShipment.DropoffType = 'REGULAR_PICKUP'
fxreq.RequestedShipment.ShipTimestamp = datetime.datetime.now()
# Special fields for the email label
fxreq.RequestedShipment.SpecialServicesRequested.SpecialServiceTypes = ('RETURN_SHIPMENT', 'PENDING_SHIPMENT')
fxreq.RequestedShipment.SpecialServicesRequested.PendingShipmentDetail.Type = 'EMAIL'
fxreq.RequestedShipment.SpecialServicesRequested.PendingShipmentDetail.ExpirationDate = formatted_date
email_address = fxreq.create_wsdl_object_of_type('EMailRecipient')
email_address.EmailAddress = quote.email
email_address.Role = 'SHIPMENT_COMPLETOR'
# RETURN SHIPMENT DETAIL
fxreq.RequestedShipment.SpecialServicesRequested.ReturnShipmentDetail.ReturnType = ('PENDING')
fxreq.RequestedShipment.SpecialServicesRequested.ReturnShipmentDetail.ReturnEMailDetail = fxreq.create_wsdl_object_of_type(
'ReturnEMailDetail')
fxreq.RequestedShipment.SpecialServicesRequested.ReturnShipmentDetail.ReturnEMailDetail.MerchantPhoneNumber = 'x-xxx-xxx-xxxx'
fxreq.RequestedShipment.SpecialServicesRequested.PendingShipmentDetail.EmailLabelDetail.Recipients = [email_address]
fxreq.RequestedShipment.SpecialServicesRequested.PendingShipmentDetail.EmailLabelDetail.Message = "Xxxxxx Xxxxxx"
fxreq.RequestedShipment.LabelSpecification = {'LabelFormatType': 'COMMON2D', 'ImageType': 'PDF'}
fxreq.RequestedShipment.Shipper.Contact.PersonName = quote.first_name + ' ' + quote.last_name
fxreq.RequestedShipment.Shipper.Contact.CompanyName = ""
fxreq.RequestedShipment.Shipper.Contact.PhoneNumber = quote.phone
fxreq.RequestedShipment.Shipper.Address.StreetLines.append(quote.address)
fxreq.RequestedShipment.Shipper.Address.City = quote.city
fxreq.RequestedShipment.Shipper.Address.StateOrProvinceCode = quote.state
fxreq.RequestedShipment.Shipper.Address.PostalCode = quote.zip
fxreq.RequestedShipment.Shipper.Address.CountryCode = settings.FEDEX_COUNTRY_CODE
fxreq.RequestedShipment.Recipient.Contact.PhoneNumber = settings.FEDEX_PHONE_NUMBER
fxreq.RequestedShipment.Recipient.Address.StreetLines = settings.FEDEX_STREET_LINES
fxreq.RequestedShipment.Recipient.Address.City = settings.FEDEX_CITY
fxreq.RequestedShipment.Recipient.Address.StateOrProvinceCode = settings.FEDEX_STATE_OR_PROVINCE_CODE
fxreq.RequestedShipment.Recipient.Address.PostalCode = settings.FEDEX_POSTAL_CODE
fxreq.RequestedShipment.Recipient.Address.CountryCode = settings.FEDEX_COUNTRY_CODE
fxreq.RequestedShipment.Recipient.AccountNumber = settings.FEDEX_ACCOUNT
fxreq.RequestedShipment.Recipient.Contact.PersonName = ''
fxreq.RequestedShipment.Recipient.Contact.CompanyName = 'Xxxxxx Xxxxxx'
fxreq.RequestedShipment.Recipient.Contact.EMailAddress = 'xxxxxx#xxxxxxxxx'
# Details of Person Who is Paying for the Shipping
fxreq.RequestedShipment.ShippingChargesPayment.PaymentType = 'SENDER'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.AccountNumber = settings.FEDEX_ACCOUNT
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Contact.PersonName = 'Xxxxx Xxxxx'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Contact.CompanyName = 'Xxxxx Xxxxxx'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Contact.PhoneNumber = 'x-xxx-xxx-xxxx'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Contact.EMailAddress = 'xxxxxxx#xxxxxxxxx'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Address.StreetLines = 'Xxxxx N. xXxxxxx'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Address.City = 'Xxxxxxx'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Address.StateOrProvinceCode = 'XX'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Address.PostalCode = 'xxxxx'
fxreq.RequestedShipment.ShippingChargesPayment.Payor.ResponsibleParty.Address.CountryCode = 'US'
# Package Info
package1 = fxreq.create_wsdl_object_of_type('RequestedPackageLineItem')
package1.SequenceNumber = '1'
package1.Weight.Value = 1
package1.Weight.Units = "LB"
package1.Dimensions.Length = box_length
package1.Dimensions.Width = box_width
package1.Dimensions.Height = box_height
package1.Dimensions.Units = "IN"
package1.ItemDescription = 'Phone'
fxreq.RequestedShipment.RequestedPackageLineItems.append(package1)
fxreq.RequestedShipment.PackageCount = '1'
try:
fxreq.send_request()
label_link = str(fxreq.response.CompletedShipmentDetail.AccessDetail.AccessorDetails[0].EmailLabelUrl)
except Exception as exc:
print('Fedex Error')
print('===========')
print(exc)
print('==========')
return label_link
Error Log
Error:cvc-datatype-valid.1.2.1: \\'2017-11-3\\' is not a valid value for \\'date\\'.\\ncvc-type.3.1.3: The value \\'2017-11-3\\' of element \\'ns0:ExpirationDate\\' is not valid."\\n }\\n }' (Error code: -1)

Getting type error for the following code

I am getting type error for the following code. The output of the code should be the graph on this page.http://www.realclearpolitics.com/epolls/other/president_obama_job_approval-1044.html . When I run the code, the error is shown in this part of the code.
reduce_the_data = new_take_page[new_colors.keys()].sum(axis=1)/100
def get_poll_data(poll_id):
url = "http://charts.realclearpolitics.com/charts/%i.xml" %int(poll_id)
return requests.get(url).text # is used to get the text from a url
def color_function(xml):
dom = web.Element(xml)
colors_dict ={}
for i in dom.by_tag('graph'):
name = i.attributes['title']
hex_colors = i.attributes['color']
colors_dict[name] = hex_colors
return colors_dict
def strip(s):
re.sub(r'[\W_]+', '', s)
def take_page(xml):
dom = web.Element(xml)
final = {}
charts_page = dom.by_tag('series')[0]
y = {i.attributes['xid']: str(i.content) for i in charts_page.by_tag('value')}
key_of_y = y.keys()
final['date'] = pd.to_datetime([y[j] for j in key_of_y])
for each_value in dom.by_tag('graph'):
title_name = each_value.attributes['title']
new_dict = {n.attributes['xid']: float(n.content)
if n.content else np.nan for n in each_value.by_tag('value')}
final[title_name] = [new_dict[k] for k in key_of_y]
finals = pd.DataFrame(final)
finals = finals.sort(columns=['date'])
return finals
def new_func(poll_id):
new_poll_id = get_poll_data(poll_id)
new_take_page= take_page(new_poll_id)
new_colors = color_function(new_poll_id)
new_take_page = new_take_page.rename(columns = {c: strip(c) for c in new_take_page.columns})
reduce_the_data = new_take_page[new_colors.keys()].sum(axis=1)/100
for x in new_colors.items():
new_take_page[x] /= reduce_the_data
for sticker, color in new_colors.items():
plt.plot(new_take_page.date, new_take_page[sticker], color = color, label= sticker)
plt.xticks(rotation= 60)
plt.legend(loc ='best')
plt.xlabel("Date")
plt.ylabel("Normalized Poll percentage")
>> new_func(1044)
>> plt.title("Polling")
TypeError: 'NoneType' object is not iterable

An strange python "if" syntax error

I get this error: Invaild syntax in my "if" statement and rly can't figur why, can anyone of you guys help me? I'm using python 3.2
here is the part of my code whit the error my code:
L = list()
LT = list()
tn = 0
players = 0
newplayer = 0
newplayerip = ""
gt = "start"
demsg = "start"
time = 1
status = 0
day = 1
conclient = 1
print("DONE! The UDP Server is now started and Waiting for client's on port 5000")
while 1:
try:
data, address = server_socket.recvfrom(1024)
if not data: break
################### reciving data! ###################
UPData = pickle.loads(data)
status = UPData[0][[0][0]
if status > 998: ##### it is here the error are given####
try:
e = len(L)
ori11 = UPData[0][1][0]
ori12 = UPData[0][1][1]
ori13 = UPData[0][1][2]
ori14 = UPData[0][1][3]
ori21 = UPData[0][1][4]
ori22 = UPData[0][1][5]
ori23 = UPData[0][1][6]
ori24 = UPData[0][1][7]
ori31 = UPData[0][2][0]
ori32 = UPData[0][2][1]
ori33 = UPData[0][2][2]
ori34 = UPData[0][2][3]
ori41 = UPData[0][2][4]
ori42 = UPData[0][2][5]
ori43 = UPData[0][2][6]
ori44 = UPData[0][2][7]
ori51 = UPData[0][3][0]
ori52 = UPData[0][3][1]
ori53 = UPData[0][3][2]
ori54 = UPData[0][3][3]
ori61 = UPData[0][3][4]
ori62 = UPData[0][3][5]
ori63 = UPData[0][3][6]
ori64 = UPData[0][3][7]
ori71 = UPData[0][4][0]
ori72 = UPData[0][4][1]
ori73 = UPData[0][4][2]
ori74 = UPData[0][4][3]
ori81 = UPData[0][4][4]
ori82 = UPData[0][4][5]
ori83 = UPData[0][4][6]
ori84 = UPData[0][4][7]
ori91 = UPData[0][5][0]
ori92 = UPData[0][5][1]
ori93 = UPData[0][5][2]
ori94 = UPData[0][5][3]
ori101 = UPData[0][5][4]
ori102 = UPData[0][5][5]
ori103 = UPData[0][5][6]
ori104 = UPData[0][5][7]
npcp11 = UPData[0][6][0]
npcp12 = UPData[0][6][1]
npcp13 = UPData[0][6][2]
npcp21 = UPData[0][6][3]
npcp22 = UPData[0][6][4]
npcp23 = UPData[0][6][5]
npcp31 = UPData[0][6][6]
npcp32 = UPData[0][6][7]
npcp33 = UPData[0][7][0]
npcp41 = UPData[0][7][1]
npcp42 = UPData[0][7][2]
npcp43 = UPData[0][7][3]
npcp51 = UPData[0][7][4]
npcp52 = UPData[0][7][5]
npcp53 = UPData[0][7][6]
npcp61 = UPData[0][7][7]
npcp62 = UPData[0][8][0]
npcp63 = UPData[0][8][1]
npcp71 = UPData[0][8][2]
npcp72 = UPData[0][8][3]
npcp73 = UPData[0][8][4]
npcp81 = UPData[0][8][5]
npcp82 = UPData[0][8][6]
npcp83 = UPData[0][8][7]
npcp91 = UPData[1][0][0]
npcp92 = UPData[1][0][1]
npcp93 = UPData[1][0][2]
npcp101 = UPData[1][0][3]
npcp102 = UPData[1][0][4]
npcp103 = UPData[1][0][5]
d0 = (status, )
d1 = (ori11,ori12,ori13,ori14,ori21,ori22,ori23,ori24)
d2 = (ori31,ori32,ori33,ori34,ori41,ori42,ori43,ori44)
d3 = (ori51,ori52,ori53,ori54,ori61,ori62,ori63,ori64)
d4 = (ori71,ori72,ori73,ori74,ori81,ori82,ori83,ori84)
d5 = (ori91,ori92,ori93,ori94,ori101,ori102,ori103,ori104)
d6 = (npcp11,npcp21,npcp31,npcp21,npcp22,npcp23,npcp31,npcp32)
d7 = (npcp33,npcp41,npcp42,npcp43,npcp51,npcp52,npcp53,npcp61)
d8 = (npcp62,npcp63,npcp71,npcp72,npcp72,npcp81,npcp82,npcp83)
d9 = (npcp91,npcp92,npcp93,npcp101,npcp102,npcp103)
pack1 = (d0,d1,d2,d3,d4,d5,d6,d7,d8)
pack2 = (d9, )
dat = pickle.dumps((pack1,pack2))
while tn < e:
server_socket.sendto(dat, (L[tn],3560))
tn = tn + 1
except:
pass
print("could not send data to some one or could not run the server at all")
else:
the part where the console tells me my error is is here:
if status > 998:

The problem is here:
status = UPData[0][[0][0]
The second opened bracket [ is not closed. The Python compiler keeps looking for the closing bracket, finds if on the next line and gets confused because if is not supposed to be inside brackets.
You may want to remove this bracket, or close it, according to your specific needs (the structure of UPData)