#LE60

[carsthatnevermadeitetc]

LE60, 2024, by Frontline Cars. The British MG restoration specialists will present a restomod MGB GT at The Quail, A Motorsports Gathering and select events during Monterey Car Week. Built to commemorate 60 years of the MGB and 30 years of Frontline, the LE60 is limited to 30 builds worldwide. Featuring an in-house modernised, 375 bhp 4.8-litre Rover V8 – the LE60 boasts a widened track, a strengthened chassis, and a custom suspension set-up by Nitron, the LE60 has a near-perfect 50/50 weight distribution.

[anotherdataanalyst]

Analysis of some Gapminder data (part3)

I revised the first code and added the first visual features.

You can find code and output below, including a short summary.

Data management decisions have been docuemtned in the code comments:

deletion of incomplete data

different clustering in order to have meaningful data

CODE:

#import necessary libraries

import pandas as pd import numpy as np

#import data set and check number of observations and variables

data = pd.read_csv('gapminder.csv', low_memory=False) number_of_orig_row = len(data) print('Some data set information:') print(len(data.columns), 'observed variables') print(number_of_orig_row, 'observations')

#convert necessary variables to numeric values

#and remove incomplete missing data

data['urbanrate']=pd.to_numeric(data['urbanrate'],'coerce') data['co2emissions']=pd.to_numeric(data['co2emissions'],'coerce') data['lifeexpectancy']=pd.to_numeric(data['lifeexpectancy'],'coerce') data.dropna(subset=['urbanrate','co2emissions','lifeexpectancy'], inplace=True)

#calculate some useful data

co2_max = data['co2emissions'].max() missing_data = number_of_orig_row - len(data) print(missing_data,'observations will be deleted since not all variables were filled with values') print()

#used variables do not include recurrent values,

#creating frequency tables out of this raw data will not help much

#instead variable data will be clustered in groups

#that can be used for frequency anaylsis

#'urbanrate' can be clustered by <25% / <50% / <75% / <100%

#'co2emissions' can be clustered by < co2_max/10000, < co2_max/1000, < co2_max/100, > co2_max/100

#'lifeexpectation' can be clustered by <60, <70, <80, <90

data['urbanrate_cl'] = data['urbanrate'].apply(lambda x: 'ur25' if x < 25 else ('ur50' if x < 50 else ('ur75' if x < 75 else 'ur100'))) data['co2emissions_cl'] = data['co2emissions'].apply(lambda x: 'ce10000' if x < co2_max/10000 else ('ce1000' if x < co2_max/1000 else ('ce100' if x < co2_max / 100 else 'ce_max'))) data['lifeexpectancy_cl'] = data['lifeexpectancy'].apply(lambda x: 'le60' if x < 60 else ('le70' if x < 70 else ('le80' if x < 80 else 'le90')))

#create and print frequency tables (with labels)

c_ur = data['urbanrate_cl'].value_counts(sort=False, normalize=False) p_ur = 100 * data['urbanrate_cl'].value_counts(sort=False, dropna=False, normalize=True) print('The count of the clustered values of variable "urbanrate" is:') print(c_ur) print() print('while the relative frequency (percentage) is in %:') print(p_ur) c_ce = data['co2emissions_cl'].value_counts(sort=False, normalize=False) p_ce = 100 * data['co2emissions_cl'].value_counts(sort=False, dropna=False, normalize=True) print() print('The count of the clustered values of variable "co2emissions" is:') print(c_ce) print() print('while the relative frequency (percentage) is in %:') print(p_ce) print() c_le = data['lifeexpectancy_cl'].value_counts(sort=False, normalize=False) p_le = 100 * data['lifeexpectancy_cl'].value_counts(sort=False, dropna=False, normalize=True) print('The count of the clustered values of variable "lifeexpectancy" is:') print(c_le) print() print('while the relative frequency (percentage) is in %:') print(p_le)

c_ur.plot(kind='bar') c_ce.plot(kind='bar') c_le.plot(kind='bar')

OUTPUT:

Some data set information: 16 observed variables 213 observations 32 observations will be deleted since not all variables were filled with values

The count of the clustered values of variable "urbanrate" is: ur25 19 ur50 54 ur75 69 ur100 39 Name: urbanrate_cl, dtype: int64

while the relative frequency (percentage) is in %: ur25 10.497238 ur50 29.834254 ur75 38.121547 ur100 21.546961 Name: urbanrate_cl, dtype: float64

The count of the clustered values of variable "co2emissions" is: ce1000 66 ce100 47 ce_max 33 ce10000 35 Name: co2emissions_cl, dtype: int64

while the relative frequency (percentage) is in %: ce1000 36.464088 ce100 25.966851 ce_max 18.232044 ce10000 19.337017 Name: co2emissions_cl, dtype: float64

The count of the clustered values of variable "lifeexpectancy" is: le60 37 le80 85 le90 22 le70 37 Name: lifeexpectancy_cl, dtype: int64

while the relative frequency (percentage) is in %: le60 20.441989 le80 46.961326 le90 12.154696 le70 20.441989 Name: lifeexpectancy_cl, dtype: float64

SHORT SUMMARY

Calculated relative frequencies indicates following:

life expectation gets its highest value in the band 70 till 80 years, showing surprisingly a minimum in the band 60 till 70.

urban rate has its highest frequency between 50 and 70%, nevertheless the contiguous bands have also high values. Less then 25% has the lowest values.

co2 emissions are very different, therefore the clusters have been chosen using scale factors (100/1000/10000 times). Countries with the lowest values have negligible emissions in comparison with the others.

[maylockhongkhihomeair]

Máy hút ẩm Beurer LE60 là một trong những giải pháp kiểm soát độ ẩm không khí trong phòng đến từ thương hiệu nổi tiếng hàng đầu tại Đức – Beurer. Máy hút ẩm không khí Beurer LE60 phù hợp cho diện tích phòng từ 40 - 60 m2. Nếu bạn sử dụng máy hút ẩm này cho gia đình thì rất phù hợp bởi hầu hết diện tích phòng tại Việt Nam cũng trong tầm này.

[le6o]

Cancun, ca 2017.

Con Hunter Thompson sulle spiagge del Messico.

Me and Hunter Thompson enjoying the mexican beaches.

[fabrif4b]

#Morgan #PlusSix #LE60 #LouwmanExclusive #morganmotor #morganmotorcompany #handbuilt #british #sportscars #britishcars #loveit https://www.instagram.com/p/CAbNl9jIr9y/?igshid=1lco1v3hi0jv4

[mikeshouts]

Morgan Motor LE60 Anniversary Cars Are The Netherlands’ Louwman Exclusive Only Cars

Love the yellow accents.

Follow us for more Tech Culture and Lifestyle Stuff.

[flyingcarpettours]

Kalabsha Temple and Nubian Museum Tour

Try Kalabsha Temple and Nubian Museum Tour, from our wide selections of Aswan Day Trips and visit Kalabsha Temple which was originally built on much earlier site that dates back to the 18th dynasty and most probably goes back to king Thothmosis and Amenhotep II and after the construction of the High Dam the temple was disassembled and reconstructed at its present location. Explore the Nubian Museum which opened its doors in November 1997 and includes three thousand pieces of antiquities representing various ages; Geological, Pharaonic, Roman, Coptic and Islamic.

Kalabsha Temple is an impressive Ptolemaic and Roman structure, not unlike nearby Philae in its layout. The early 19th-century Swiss traveller Burckhardt (who rediscovered Abu Simbel) thought it was 'amongst the most precious remains of Egyptian antiquity'. The temple, started in the late Ptolemaic period and completed during the reign of Emperor Augustus (30 BC–AD 14), was dedicated to the Nubian solar god Merwel, known to the Greeks as Mandulis. Later it was used as a church.

An impressive stone causeway leads from the lake to the first pylon of the temple, beyond which are the colonnaded court and the eight-columned hypostyle hall. Inscriptions on the walls show various emperors or pharaohs in the presence of gods and goddesses. Just beyond the hall is the sanctuary, consisting of three chambers. Stairs from one chamber lead up to the roof, from where there are superb views of Lake Nasser and the High Dam, across the capitals of the hall and court. An inner passage, between the temple and the encircling wall, leads to a well-preserved Nilometer. The temple's original outer stone gateway was given by the Egyptian government to Germany in 1977, in thanks for helping to move this building. It is now in the Egyptian Museum, Berlin.

As a result of a massive UNESCO effort, the temples here were transplanted from a now-submerged site about 50km south of Aswan. The new site is on the west bank of Lake Nasser just south of the High Dam.

When the water level is low you can sometimes walk across to the site; otherwise, you can find a motor boat on the western side of the High Dam (around LE60 for the return trip and an hour to visit).

Then visit Nubia Museum, opposite Basma Hotel, is a treat, a showcase of the history, art and culture of Nubia. Established in 1997 in cooperation with UNESCO, the museum is a reminder of what was lost beneath Lake Nasser. Exhibits are beautifully displayed in huge halls, where clearly written explanations take you from 4500 BC through to the present day.

The exhibits start with prehistoric artefacts and objects from the Kingdom of Kush and Meroe. Coptic and Islamic art displays lead to a description of the massive UNESCO project to move Nubia’s most important historic monuments away from the rising waters of Lake Nasser, following the building of the Aswan High Dam.

Among the museum highlights are 6000-year-old painted pottery bowls and an impressive quartzite statue of a 25th-dynasty priest of Amun in Thebes with distinct Kushite (Upper Nubian) features. The stunning horse armour found in tombs from the Ballana period (5th to 7th centuries BC) shows the sophistication of artisanship during this brief ascendancy. A fascinating display traces the development of irrigation along the Nile, from the earliest attempts to control the flow of the river, right up to the building of the old Aswan Dam. A model of a Nubian house, complete with old furniture and mannequins wearing traditional silver jewellery, attempts to portray the folk culture of modern Nubia.

Enjoy Kalabsha Temple and Nubian Museum Tour

Flying Carpet Tours delegate will pick you up at your hotel in Aswan, and you’ll be transferred by a private A/C vehicle to the enchanting Temple of Kalabsha which is located close to Lake Nasser, near the western end of Aswan High Dam. Unleash your inner and know more about the temple that was built by Toutmosis II and Amenophis II at Kalabsha 50 km south of Aswan and then disassembled and reconstructed at its present location after the construction of the High Dam. Feast your soul and move to explore the Nubian Museum which was opened its doors in November 1997 and includes three thousand pieces of antiquities. Finally Flying Carpet Tours guide will escort you back to your hotel in Aswan.

Included

• Pick up and drop off to your hotel in Aswan

• Excursion to Kalabsha Temple and Nubian Museum

• Entrance fees to the above mentioned sites

• English speaking guide to the sites mentioned above    

• Bottle of Mineral Water during the Excursion

• All transfers by air-conditioned Van

• All service charges and taxes

Excluded

• Visa to Egypt

• Any optional tours required

• Tipping

For more info about Kalabsha Temple and Nubian Museum Tour:

E-mail: sales@flyingcarpettours.com

Website: www.flyingcarpettours.com

Tel.: +201099906242

[yamandus]

Morgan Plus Six Blauw LE60, 2020. A special edition of 2 cars finished in Ice Blue with yellow highlights to celebrate 60 years of Morgan’s partnership with Dutch dealership Louwman Exclusive

[carsthatnevermadeitetc]

Morgan Plus Six Grijs LE60, 2020. A special edition of 2 cars finished in Scintilla Silver with yellow highlights celebrating 60 years of Morgan’s partnership with Dutch dealership Louwman Exclusive

[anotherdataanalyst]

Analysis of some Gapminder data (part2)

I started the analysis of the gapminder data set, based on first Python code learnings.

The code looks like this:

#import necessary libraries

import pandas as pd import numpy as np

#import data set and check number of observations and variables

data = pd.read_csv('gapminder.csv', low_memory=False) print(len(data)) print(len(data.columns))

#convert necessary variables to numeric values

#and remove incomplete missing data

data['urbanrate']=pd.to_numeric(data['urbanrate'],'coerce') data['co2emissions']=pd.to_numeric(data['co2emissions'],'coerce') data['lifeexpectancy']=pd.to_numeric(data['lifeexpectancy'],'coerce') data.dropna(subset=['urbanrate','co2emissions','lifeexpectancy'], inplace=True)

#calculate some useful data

co2_max = data['co2emissions'].max()

#used variables do not include recurrent values,

#creating frequency tables out of this raw data will not help much

#instead variable data will be clustered in groups

#that can be used for frequency anaylsis

#'urbanrate' can be clustered by <25% / <50% / <75% / <100%

#'co2emissions' can be clustered by < co2_max/10000, < co2_max/1000, < co2_max/100, > co2_max/100

#'lifeexpectation' can be clustered by <60, <70, <80, <90

data['urbanrate_cl'] = data['urbanrate'].apply(lambda x: 'ur25' if x < 25 else ('ur50' if x < 50 else ('ur75' if x < 75 else 'ur100'))) data['co2emissions_cl'] = data['co2emissions'].apply(lambda x: 'ce10000' if x < co2_max/10000 else ('ce1000' if x < co2_max/1000 else ('ce100' if x < co2_max / 100 else 'ce_max'))) data['lifeexpectancy_cl'] = data['lifeexpectancy'].apply(lambda x: 'le60' if x < 60 else ('le70' if x < 70 else ('le80' if x < 80 else 'le90')))

#create and print frequency tables (with labels)

c_ur = data['urbanrate_cl'].value_counts(sort=True, normalize=False) p_ur = data['urbanrate_cl'].value_counts(sort=False, dropna=False, normalize=True) print('The count of the clustered variable "urbanrate" is:') print(c_ur) print() print('while the relative frequency is:') print(p_ur) c_ce = data['co2emissions_cl'].value_counts(sort=True, normalize=False) p_ce = data['co2emissions_cl'].value_counts(sort=False, dropna=False, normalize=True) print() print('The count of the clustered variable "co2emissions" is:') print(c_ce) print() print('while the relative frequency is:') print(p_ce) print() c_le = data['lifeexpectancy_cl'].value_counts(sort=True, normalize=False) p_le = data['lifeexpectancy_cl'].value_counts(sort=False, dropna=False, normalize=True) print() print('The count of the clustered variable "lifeexpectancy" is:') print(c_le) print() print('while the relative frequency is:') print(p_le)

The output looks like this.

213 16

The count of the clustered variable "urbanrate" is: ur75 69 ur50 54 ur100 39 ur25 19 Name: urbanrate_cl, dtype: int64

while the relative frequency is: ur25 0.104972 ur50 0.298343 ur75 0.381215 ur100 0.215470 Name: urbanrate_cl, dtype: float64

The count of the clustered variable "co2emissions" is: ce1000 66 ce100 47 ce10000 35 ce_max 33 Name: co2emissions_cl, dtype: int64

while the relative frequency is: ce1000 0.364641 ce100 0.259669 ce_max 0.182320 ce10000 0.193370 Name: co2emissions_cl, dtype: float64

The count of the clustered variable "lifeexpectancy" is: le80 85 le60 37 le70 37 le90 22 Name: lifeexpectancy_cl, dtype: int64

while the relative frequency is: le60 0.204420 le80 0.469613 le90 0.121547 le70 0.204420 Name: lifeexpectancy_cl, dtype: float64

Missing data was filtered out in order to get representative values.

Calculated relative frequencies indicates following:

life expectation is higher in the band 70 till 80 years

urban rate has its highest frequency between 50 and 70%, nevertheless the contiguous bands have also high values. Less then 25% has the lowest values.

co2 emissions are very different, therefore the clusters have been chosen using scale factors (100/1000/10000 times). Countries with the lowest values have negligible emissions in comparison with the others.

[maylockhongkhihomeair]

Nấm mốc, rỉ sét và mùi mốc không có cơ hội xảy ra với máy hút ẩm Beurer LE60 MADE IN GERMANY. Nó loại bỏ độ ẩm dư thừa trong không khí và đảm bảo độ ẩm không khí tương đối lý tưởng trong phòng. Sản phẩm máy hút ẩm không khí Beurer LE60 này được giới chuyên môn đánh giá cao và là một trong những giải pháp lý tưởng đối đầu với mùa nồm ẩm hay mùa mưa nhiều tại Việt Nam.

[cardaily]

Morgan Unveils Limited Edition LE60 Plus Six And 3-Wheeler @ CarDaily.Co #galleries #morgan #newcars #cars #carsofinstagram #carporn #carlifestyle #carnewsnetwork #carswithoutlimits #carspotting #dailycar #cardaily

[usedcarshoustontx]

Morgan Unveils Limited Edition LE60 Plus Six And 3-Wheeler

[dzureiku1983]

Desarticulada una red de narcotráfico en Colombia operada por controladores aéreos https://t.co/le60EwujFC Las autoridades colombianas investigan el presunto vínculo con el cartel de Sinaloa copyright © 2018 January 14, 2019 at 07:52AM

Desarticulada una red de narcotráfico en Colombia operada por controladores aéreos https://t.co/le60EwujFC Las autoridades colombianas investigan el presunto vínculo con el cartel de Sinaloa copyright © 2018 January 14, 2019 at 07:52AM

— Utah, USA (@ybuasifogy) January 14, 2019

from Twitter https://twitter.com/ybuasifogy January 14, 2019 at 07:57AM via IFTTT

[nelisebc-blog]

"It's been a while since you discovered the solution, but at that moment, it seemed too good to be true. But the stated idea took shape, the tunnel ended and the light arrived. Congratulations!" http://tarotlife.com/TOnline/Arcanos/Texto/LE60.html https://www.instagram.com/p/CQHeaviHl08/?utm_medium=tumblr

[coolvirtualstudentgentlemen-blog]

【悲報】千鳥、徐々にコンビ格差が生まれ始めるｗｗｗｗ

1：風吹けば名無し： 2019/01/29(火) 10:29:41.98ID:Ljr3D0Qo0.net ノブ←ゴチ新メンバー、ピンのレギュラー多数

大悟←志村のチ●コしゃぶって得たレギュラー1本

2：風吹けば名無し： 2019/01/29(火) 10:30:19.67ID:6QlLrV7Ga.net ノブゴチ新メンバーなんか

3：風吹けば名無し： 2019/01/29(火) 10:30:25.61ID:rwqb+lE60.net でも大悟には何かがあるから

7：風吹けば名無し： 2019/01/29(火) 10:31:47.61ID:5QbhgI8DM.net 大吾ってノブありきだよな

9：風吹けば名無し： 2019/01/29(火) 10:32:05.90ID:GfiZm4QRd.net でも二人がいちばんおもろい

14：風吹けば名無し： 2019/01/2…