In [1]:
import numpy as np
import pandas as pd
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webpage = "https://nssdc.gsfc.nasa.gov/planetary/factsheet/planet_table_ratio.html"
webpage
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tables = pd.read_html(webpage, header = 0)
tables
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ss = tables[0]
ss
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In [5]:
ss.head()
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In [6]:
ss[0:5:2]
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In [7]:
ss[0:10:3]
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ss[10:19:3]
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ss[:]
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ss[-4:]
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ss[-10:]
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As you will see, it is a range of integers, from 0 to 19.
Please note that range(0, 19) starts with 0 and ends at 18. It does not include 19.
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ss.index
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ss.columns
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ss.rename(columns =
{'Unnamed: 0': 'properties'},
inplace=True)
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ss.columns
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ss = ss.set_index('properties')
ss
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In [17]:
ss.index
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In [18]:
ss.columns
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In [19]:
ss.T
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ss
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In [21]:
ss = ss.T
ss
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In [22]:
ss.index.names = ['name_of_planet']
ss
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In [23]:
ss.dtypes
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In [24]:
ss.Gravity.astype(float)
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See the mass of Jupiter, it is huge.
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ss.Gravity.astype(float).plot()
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We can see that Jupiter has almost three times the mass of all other planets put together.
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ss.Gravity.astype(float).plot.pie()
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ss.Gravity.astype(float).plot.bar()
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ss['log_Gravity'] = np.log(ss.Gravity.astype(float))
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ss.columns
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In [30]:
ss[['Gravity', 'log_Gravity']]
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As the above data is comparison with the Earth, its value is 0. Smaller than the Earth are negative and others positive.
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ss.log_Gravity.plot.bar()
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ss['log_Mass'] = np.log(ss.Mass.astype(float) / float(ss.Mass['PLUTO']))
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ss[['Mass', 'log_Mass']]
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Now all values are positive as they are larger than Pluto
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ss.log_Mass.plot.bar()
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ss.log_Mass.plot.pie()
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In [36]:
ss['log_Mass'] = np.log(ss.Mass.astype(float) / float(ss.Mass['PLUTO'])* 2 )
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ss['log_Mass']
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ss.log_Mass.plot.bar()
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In [39]:
ss.log_Mass.plot.pie()
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In [40]:
ss["Distance from Sun"]
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We convert first to str, then replace *
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ss["Distance from Sun"] = ss["Distance from Sun"].str.replace('\*', '')
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ss["Distance from Sun"]
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In [43]:
ss["Distance from Sun"].astype(float).plot.pie()
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In 'Surface Pressure', we have some 'Unknown*' values. We will remove them to plot charts.
Let us first create a separate series of Surface Pressure only so that we don't change our original data.
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ss_sp = ss["Surface Pressure"]
ss_sp
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In [45]:
ss_sp = ss_sp[~ss_sp.str.contains("Unknown")]
All records with 'Unknown' are removed.
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ss_sp
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In [47]:
ss_sp.astype(float).plot.bar()
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In [48]:
ss_sp.astype(float).plot.pie()
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