cab938

absl-py==2.2.2
aiofiles==24.1.0
aiohappyeyeballs==2.6.1
aiohttp==3.11.18
aiosignal==1.3.2
alembic==1.15.2
altair==5.5.0
annotated-types==0.7.0
anyio==4.9.0
argon2-cffi==23.1.0

cab938

aiofiles==23.2.1
aiohappyeyeballs==2.4.3
aiohttp==3.10.10
aiosignal==1.3.1
altair==5.4.1
annotated-types==0.7.0
anyio==4.6.0
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0

cab938

146.204.224.152 - feest6811 [21/Jun/2019:15:45:24 -0700] "POST /incentivize HTTP/1.1" 302 4622
197.109.77.178 - kertzmann3129 [21/Jun/2019:15:45:25 -0700] "DELETE /virtual/solutions/target/web+services HTTP/2.0" 203 26554
156.127.178.177 - okuneva5222 [21/Jun/2019:15:45:27 -0700] "DELETE /interactive/transparent/niches/revolutionize HTTP/1.1" 416 14701
100.32.205.59 - ortiz8891 [21/Jun/2019:15:45:28 -0700] "PATCH /architectures HTTP/1.0" 204 6048
168.95.156.240 - stark2413 [21/Jun/2019:15:45:31 -0700] "GET /engage HTTP/2.0" 201 9645
71.172.239.195 - dooley1853 [21/Jun/2019:15:45:32 -0700] "PUT /cutting-edge HTTP/2.0" 406 24498
180.95.121.94 - mohr6893 [21/Jun/2019:15:45:34 -0700] "PATCH /extensible/reinvent HTTP/1.1" 201 27330
144.23.247.108 - auer7552 [21/Jun/2019:15:45:35 -0700] "POST /extensible/infrastructures/one-to-one/enterprise HTTP/1.1" 100 22921
2.179.103.97 - lind8584 [21/Jun/2019:15:45:36 -0700] "POST /grow/front-end/e-commerce/robust HTTP/2.0" 304 14641
241.114.184.133 - tromp8355 [21/Jun/2019:15

cab938

Ronald Mayr: A
Bell Kassulke: B
Jacqueline Rupp: A 
Alexander Zeller: C
Valentina Denk: C 
Simon Loidl: B 
Elias Jovanovic: B 
Stefanie Weninger: A 
Fabian Peer: C 
Hakim Botros: B

cab938

#!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/asia/land/ytd/12/1910-2016', header=0)[0]
pop1=df[df['Year']<1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)
pop2=df[df['Year']>=1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))

cab938

!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/asia/land/ytd/12/1910-2016', header=0)[0]
pop1 = #put the cleaned list of all temperature anomalies for pre 1950
pop2 = #put the cleaned list of all temperature anomalies for 1950 and above

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))

cab938

#!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/northAmerica/land/ytd/12/1880-2016', header=0)[0]
pop1=df[df['Year']<1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)
pop2=df[df['Year']>=1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))

cab938

!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/northAmerica/land/ytd/12/1880-2016', header=0)[0]
pop1 = #put the cleaned list of all temperature anomalies for pre 1950
pop2 = #put the cleaned list of all temperature anomalies for 1950 and above

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))

cab938

#!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart#
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/northAmerica/land/ytd/12/1880-2016', header=0)[0]
pop1=df[df['Year']<1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)
pop2=df[df['Year']>=1950]['Anomaly(1910-2000 Base Period)'].apply(lambda x: x.split('°C')[0]).astype(float)

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))

cab938

!pip install html5lib #install html5lib, only needs to be run once
#You might need to restart kernel after running with the menu Kernel>Restart
import pandas as pd
import numpy as np
from scipy import stats
df=pd.read_html('https://proxy.mentoracademy.org/getContentFromUrl/?userid=user&url=https://www.ncdc.noaa.gov/cag/global/time-series/globe/land/ytd/12/1910-2016', header=0)[0]
pop1 = #put the cleaned list of all temperature anomalies for pre 1950
pop2 = #put the cleaned list of all temperature anomalies for 1950 and above

print("Mean anomaly values before 1950 {}, and mean after 1950 {}".format(np.mean(pop1),np.mean(pop2)))