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Pandas for IT-2

Vi har i dette faget jobbet med JavaScript. Dette er et programmeringsspråk som fungerer bra for å innhente og presentere reelle datasett. Men når man skal analysere data så er ikke JavaScript et spesielt godt verktøy. Da er det nyttig å bruke et verktøy som heter Pandas. Dette verktøyet er laget for Python. Vi skal derfor gjøre noe morsomt. Vi skal bruke noe som heter PyScript. Dette er Python i nettleseren. Hvordandette gjøres er besrkvet i psdf dokumentet pandas.pdf som du finner under filer idette repositoriet. I dette pdf dokumenteter det flere kodesnutter. Siden disse ikke kan lastes ned er disse kodesnuttene lagt inn her og kan enkelt lastes ned. Sidetallene henviser til sidetallet i pdf dokumentet.

Hovedkode

Hovedkoden er et HTML dokument (se s. 2). Vi legger Python koden vår inn i taggen som heter . Når vi bruker taggen vil Pythnon koden vises på nettsiden i en REPL (Read Evaluate Print Loop, ala det vi har i Jupyter Notebook). Dette kan vi endre ved på erstate taggen med en tagg.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Pyscript</title>
    <!-- Her leser vi inn pyscript biblioteket -->
    <script defer src="https://pyscript.net/latest/pyscript.js"></script>
</head>
<style>

</style>
<body>
<!-- I <py-config> taggen installerer vi pandas og andre aktuelle bibliotek -->
<py-config>
    packages = ["pandas","matplotlib","jinja2","requests"]
</py-config>
<!--
Inne i <py-repl> taggen kan vi skrive vanlig Python kode.
Når vi bruker repl så får vi vist Pyrthon koden i nettleseren
<py-repl> kan erstatters med <py-script>. Da vises ikke python koden.
-->
<py-repl>
    print("Hello World")
</py-repl>
</body>
</html>

kode s. 3:

    import pandas as pd
    import jinja2
    import matplotlib.pyplot as plt
    from pyodide.http import open_url
    import json
    data=[{"navn":"Per","adresse":"skoleveien 5"},{"navn":"Ola","adresse":"skoleveien 6"}]
    df=pd.DataFrame(data)
    display(df)

kode nr. 1 s. 6:

    import pandas as pd
    import jinja2
    import matplotlib.pyplot as plt
    from pyodide.http import open_url
    import json
    data=[{"navn":"Per","adresse":"skoleveien 5"},{"navn":"Ola","adresse":"skoleveien 6"}]
    df=pd.DataFrame(data)
    display(df.loc[:,["navn"]])

kode nr. 2 s. 6:

    import pandas as pd
    import jinja2
    import matplotlib.pyplot as plt
    from pyodide.http import open_url
    import json
    data=[{"navn":"Per","adresse":"skoleveien 5"},{"navn":"Ola","adresse":"skoleveien 6"}]
    df=pd.DataFrame(data)
    display(df.loc[[0],["navn"]])

kode nr. 3 s. 6:

    import pandas as pd
    import jinja2
    import matplotlib.pyplot as plt
    from pyodide.http import open_url
    import json
    data=[{"navn":"Per","adresse":"skoleveien 5"},{"navn":"Ola","adresse":"skoleveien 6"}]
    df=pd.DataFrame(data)
    display(df.loc[[0,1],["navn"]])

kode nr. 1s. 7:

    import pandas as pd
    import jinja2
    import matplotlib.pyplot as plt
    from pyodide.http import open_url
    import json
    data=[{"navn":"Per","adresse":"skoleveien 5"},{"navn":"Ola","adresse":"skoleveien 6"}]
    df=pd.DataFrame(data)
    display(df.loc[[1,0],["navn"]])

kode s. 9:

    import pandas as pd
    import jinja2
    import matplotlib.pyplot as plt
    from pyodide.http import open_url
    import json
    data=[
            {"navn":"Per","adresse":"skoleveien 5","alder":;45,"hoyde":176,"vekt":79},
            {"navn":"Ola","adresse":"skoleveien 6","alder":56,"hoyde":186,"vekt":89},
            {"navn":"Hans","adresse":"skoleveien 8","alder":66,"hoyde":197,"vekt":99}
    ]
    df=pd.DataFrame(data)
    display(df)

kode s. 10:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   data=[
            {"navn":"Per","adresse":"skoleveien 5","alder":;45,"hoyde":176,"vekt":79},
            {"navn":"Ola","adresse":"skoleveien 6","alder":56,"hoyde":186,"vekt":89},
            {"navn":"Hans","adresse":"skoleveien 8","alder":66,"hoyde":197,"vekt":99}
    ]
    df=pd.DataFrame(data)    
    farge=['r','g','#0000FF']
    linje=['g','b','r']
    fig,ax=plt.subplots(figsize=(8,3),layout='constrained')
    for i i range (0,len(df.index)):
        ax.bar(df.loc[i,"navn"],df.loc[i,"vekt"],color=farge[i],edgecolor=linje[i],df.loc[i,"vekt"],width=0.2)
    ax.legend(bbox_to_anchor=(1,1))
    ax.set_title("Vekt for ulike personer")
    display(plt)

kode s. 12:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   df=pd.read_json(open_url("navn.json"))
   farge=['r','g','#0000FF']
   linje=['g','b','r']
   fig,ax=plt.subplots(figsize=(8,3),layout='constrained')
   for i i range (0,len(df.index)):
       ax.bar(df.loc[i,"navn"],df.loc[i,"vekt"],color=farge[i],edgecolor=linje[i],df.loc[i,"vekt"],width=0.2)
   ax.legend(bbox_to_anchor=(1,1))
   ax.set_title("Vekt for ulike personer")
   display(plt)

kode nr. 1 s. 14:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   df=pd.read_json(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   display(df)

kode nr. 2 s. 14:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   df=pd.read_json(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   display(df['values'])

kode s. 15:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   data=json.load(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   df=pd.json_normalize( data,record_path=["values"])
   display(df)

kode s. 16:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   data=json.load(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   df=pd.json_normalize(data,record_path=["values"],meta=["station","zone"])
   df1=df[['fromTime','toTime','value','color','station']]
   display(df1)

kode s. 19:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   def row_style(row):
       return pd.Series(
       'background-color:#'+row.color+';'+
       'color:blue;'+
       'font-size:20px;'
       ,row.index)
   data=json.load(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   df=pd.json_normalize(data,record_path='values')
   df1=df[['fromTime','toTime','value','color']]
   display(df1.style.apply(row_style, axis=1))

kode s. 21

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   def row_style(row):
       return pd.Series(
       'background-color:#'+row.color+';'+
       'color:blue;'+
       'font-size:20px;'
       ,row.index)
   data=json.load(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   df=pd.json_normalize(data,record_path='values')
   df1=df[['fromTime','toTime','value','color']]
display(df1.style.apply(row_style, axis=1).hide(axis="columns",subset='color').hide(axis="index").set_table_styles(
    [
        {'selector':'th,td','props':
         'border: 1px solid black;'    
        }
    ]
 ).set_table_attributes('style="border-collapse:collapse"'))

kode s. 23:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   data=json.load(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   df=pd.json_normalize(data,record_path='values')
   avstand=-0.3
   fig,ax=plt.subplots(figsize=(11,6),layout='constrained') 
   for i in range (0,len(df.index)):
       ax.bar(df.loc[i,"fromTime"],df.loc[i,"value"],color="#"+df.loc[i,"color"],width=0.5)
   disply(plt)

Kode s. 25:

   import pandas as pd
   import jinja2 
   import matplotlib.pyplot as plt
   from pyodide.http import open_url
   import json
   data=json.load(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   df=pd.json_normalize(data,record_path='values')
   avstand=-0.3
   fig,ax=plt.subplots(figsize=(11,6),layout='constrained') 
   for i in range (0,len(df.index)):
       ax.bar(df.loc[i,"fromTime"],df.loc[i,"value"],color="#"+df.loc[i,"color"],width=0.5)
       ax.text(avstand,df.loc[i,"value"]+1,df.loc[i,"value"])
       avstand=avstand+1
    plt.xticks(rotation=90)
    plt.title('luftkvalitetsindeks')
    plt.ylabel('luftkvalitetsindeks (mikrogram/m^3')
    plt.xlabel('dato')
   disply(plt)

kode s. 27:

   import pandas as pd
   import jinja2 
   import matplotlib.pyploy as plt
   from pyodide.http import open_url
   import json
   data=json.load(open_url("https://api.nilu.no/aq/historical/2022-02-02/2022-02-03/ids/3124"))
   df=pd.json_normalize(data,record_path='values') 
   df2=df['index'].value_counts().div(len(df.index)).multiply(100)
   fig,ax=plt.subplots()
   ax.pie(df2,labels=['lav','moderat','høy'],autopct='%1.1f%%',colors=['#6ee86e','#ff9900','#ff0000']) 
   display(plt)

kode s. 29

import js
import jinja2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pyodide.http import open_url
df=pd.read_json(open_url("./05.json"))
s=df.start_station_name.value_counts()
mest_pop=s.head(3)
minst_pop=s.tail(3)
s=pd.concat([mest_pop,minst_pop])
fig,ax=plt.subplots()
ax.bar(s.index,s,color=['r','g','b','y','c','m'])
plt.title('Tre mest og minst populære startlokasjoner')
plt.xlabel('startlokasjon')
plt.ylabel('Antall (popularitet)')
plt.xticks(rotation=30)
plt.tight_layout()
display(plt)

Kode s. 30

import js
import jinja2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pyodide.http import open_url
df=pd.read_json(open_url("./05.json"))
df2=df.groupby('end_station_name').mean(numeric_only="True").sort_values('duration',ascending=False).head(3)
df2['min']=df2['duration'].div(60).round(0)
fig,ax=plt.subplots()
ax.bar(df2.index,df2['min'],color=['r','g','b'])
plt.xlabel('sluttlokasjon')
plt.ylabel('Minutter')
plt.title('Sluttlokasjonene med høyeste gjennmsnittsvarighet')
display(plt)

kode s. 31

import js
import jinja2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pyodide.http import open_url
df=pd.read_json(open_url("./05.json"))
df['dato']=pd.to_datetime(df['started_at'])
df['ukedag_nr']=df['dato'].dt.weekday
s=df['ukedag_nr'].value_counts().sort_index()
dager=['mandag','tirsdag','onsdag','torsdag','fredag','lørdag','søndag']
fig,ax=plt.subplots()
ax.bar(dager,s,color=['r','b','g','c','m','y','k'])
plt.title('Antall turer totalt per dag')
plt.ylabel('Antall turer')
display(plt)

kode s. 32

import js
import jinja2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pyodide.http import open_url
df=pd.read_json(open_url("./05.json"))
s=df.start_station_name.value_counts()
mest_pop=s.head(3)
minst_pop=s.tail(3)
s=pd.concat([mest_pop,minst_pop])
fig,ax=plt.subplots()
ax.bar(s.index,s,color=['r','g','b','y','c','m'])
for bars in ax.containers:
    ax.bar_label(bars)
plt.title('Tre mest og minst populære startlokasjoner')
plt.xlabel('startlokasjon')
plt.ylabel('Antall (popularitet)')
plt.xticks(rotation=30)
plt.tight_layout()
display(plt)

kode s. 33

import js
import jinja2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pyodide.http import open_url
df=pd.read_json(open_url("./05.json"))
df2=df.groupby('end_station_name').mean().sort_values('duration',ascending=False).head(3)
df2['min']=df2['duration'].div(60).round(0)
fig,ax=plt.subplots()
ax.bar(df2.index,df2['min'],color=['r','g','b'])
for bars in ax.containers:
    ax.bar_label(bars)
plt.xlabel('Sluttlokasjon')
plt.ylabel('Minutter')
plt.title('Sluttlokasjoner med høyeste gjennomsnittsvarighet')
display(plt)

kode s. 34

import js
import jinja2
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pyodide.http import open_url
df=pd.read_json(open_url("./05.json"))
df['dato']=pd.to_datetime(df['started_at'])
df['ukedag_nr']=df['dato'].dt.weekday
s=df['ukedag_nr'].value_counts().sort_index()
dager=['mandag','tirsdag','onsdag','torsdag','fredag','lørdag','søndag']
fig,ax=plt.subplots()
ax.bar(dager,s,color=['r','g','b','c','m','y','k'])
for bars in ax.containers:
    ax.bar_label(bars)
plt.title('Antall turer totalt per dag')
plt.ylabel('Antall turer')
display(plt)

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