Raw Analysis

Imports

In [1]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from scipy import stats
import plotly.express as px
from geopy.geocoders import Nominatim
import geopy as gp
from datetime import datetime

Data

In [3]:
data = pd.read_csv('data/meteorite-landings.csv')
print(data.columns)

data.head()

Index(['name', 'id', 'nametype', 'recclass', 'mass', 'fall', 'year', 'reclat',
       'reclong', 'GeoLocation'],
      dtype='object')
Out[3]:
name id nametype recclass mass fall year reclat reclong GeoLocation
0 Aachen 1 Valid L5 21.0 Fell 1880.0 50.77500 6.08333 (50.775000, 6.083330)
1 Aarhus 2 Valid H6 720.0 Fell 1951.0 56.18333 10.23333 (56.183330, 10.233330)
2 Abee 6 Valid EH4 107000.0 Fell 1952.0 54.21667 -113.00000 (54.216670, -113.000000)
3 Acapulco 10 Valid Acapulcoite 1914.0 Fell 1976.0 16.88333 -99.90000 (16.883330, -99.900000)
4 Achiras 370 Valid L6 780.0 Fell 1902.0 -33.16667 -64.95000 (-33.166670, -64.950000)

Quick Look

In [5]:
print("Data described: \n")
print(data.describe())
print('\n')
print("Data info: \n")
print(data.info())
print('\n')
print("Data types: \n")
print(data.dtypes)

Data described: 

                 id          mass          year        reclat       reclong
count  45716.000000  4.558500e+04  45428.000000  38401.000000  38401.000000
mean   26889.735104  1.327808e+04   1991.772189    -39.122580     61.074319
std    16860.683030  5.749889e+05     27.181247     46.378511     80.647298
min        1.000000  0.000000e+00    301.000000    -87.366670   -165.433330
25%    12688.750000  7.200000e+00   1987.000000    -76.714240      0.000000
50%    24261.500000  3.260000e+01   1998.000000    -71.500000     35.666670
75%    40656.750000  2.026000e+02   2003.000000      0.000000    157.166670
max    57458.000000  6.000000e+07   2501.000000     81.166670    354.473330


Data info: 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45716 entries, 0 to 45715
Data columns (total 10 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   name         45716 non-null  object 
 1   id           45716 non-null  int64  
 2   nametype     45716 non-null  object 
 3   recclass     45716 non-null  object 
 4   mass         45585 non-null  float64
 5   fall         45716 non-null  object 
 6   year         45428 non-null  float64
 7   reclat       38401 non-null  float64
 8   reclong      38401 non-null  float64
 9   GeoLocation  38401 non-null  object 
dtypes: float64(4), int64(1), object(5)
memory usage: 3.5+ MB
None


Data types: 

name            object
id               int64
nametype        object
recclass        object
mass           float64
fall            object
year           float64
reclat         float64
reclong        float64
GeoLocation     object
dtype: object
In [8]:
data.nametype.value_counts()
# Under NameType, 'valid' is for most meteorites and 'relict'
# are for objects that were once meteorites but are now highly
# altered by weathering on
Out[8]:
Valid     45641
Relict       75
Name: nametype, dtype: int64

Cleaning

In [10]:
# rename columns
data.rename(columns={'recclass':'class', 'reclat':'lat', 'reclong':'long', 'mass (g)':'mass'}, inplace=True)
data.head()
Out[10]:
name id nametype class mass fall year lat long GeoLocation
0 Aachen 1 Valid L5 21.0 Fell 1880.0 50.77500 6.08333 (50.775000, 6.083330)
1 Aarhus 2 Valid H6 720.0 Fell 1951.0 56.18333 10.23333 (56.183330, 10.233330)
2 Abee 6 Valid EH4 107000.0 Fell 1952.0 54.21667 -113.00000 (54.216670, -113.000000)
3 Acapulco 10 Valid Acapulcoite 1914.0 Fell 1976.0 16.88333 -99.90000 (16.883330, -99.900000)
4 Achiras 370 Valid L6 780.0 Fell 1902.0 -33.16667 -64.95000 (-33.166670, -64.950000)

Sampling

(Test sample size is set to 10%)

Only comment out once all testing / experiment is done, to use real data

In [11]:
# *****************************
# HIGHLY IMPORTANT
# *****************************

# Sample data
print("Original Data Stats: \n")
print(data.describe())

print('\n--------\n')

print("New Sample Data Stats: \n")
data['year'].fillna(0).astype(int)
data['mass'].fillna(0).astype(int)


data = data.sample(frac=0.1)  # 10% sample set
print(data.describe())

Original Data Stats: 

                 id          mass          year           lat          long
count  45716.000000  4.558500e+04  45428.000000  38401.000000  38401.000000
mean   26889.735104  1.327808e+04   1991.772189    -39.122580     61.074319
std    16860.683030  5.749889e+05     27.181247     46.378511     80.647298
min        1.000000  0.000000e+00    301.000000    -87.366670   -165.433330
25%    12688.750000  7.200000e+00   1987.000000    -76.714240      0.000000
50%    24261.500000  3.260000e+01   1998.000000    -71.500000     35.666670
75%    40656.750000  2.026000e+02   2003.000000      0.000000    157.166670
max    57458.000000  6.000000e+07   2501.000000     81.166670    354.473330

--------

New Sample Data Stats: 

                 id          mass         year          lat         long
count   4572.000000  4.562000e+03  4536.000000  3793.000000  3793.000000
mean   27031.823053  3.495610e+04  1991.572090   -39.626677    64.362791
std    16886.665968  1.280904e+06    25.529171    46.221709    80.515703
min        6.000000  0.000000e+00  1583.000000   -86.933330  -165.433330
25%    12851.750000  7.100000e+00  1987.000000   -76.716670     0.000000
50%    24277.500000  3.221000e+01  1998.000000   -71.500000    54.196670
75%    44400.250000  2.140000e+02  2003.000000     0.000000   157.166670
max    57434.000000  6.000000e+07  2013.000000    76.133330   175.730280

Plotting

Fall vs. Fallen

In [12]:
data['fall'].hist(bins=3)  # 
plt.show()

Top 10 classifications of meteors

In [13]:
top_10_class = data['class'].value_counts()[:10]
plt.bar(top_10_class, height = 1)

top_10_class.plot(kind='bar')
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x11d653ed0>

Geolocation Conversion

In [14]:
geolocator = Nominatim(user_agent="project_impact")

lists = []
for i in range(20):
    lats = data['lat'].get(key = i)
    longs = data['long'].get(key = i)
    coor = gp.Point(lats, longs)
    country = geolocator.reverse(gp.Point(coor)).raw['address'].get('country')
    lists.append(country)
print(lists)

[None, None, 'Canada', None, None, None, None, None, None, None, None, None, None, None, None, 'Nigeria', None, None, None, None]

Year vs Mass

In [18]:
fig, ax = plt.subplots(figsize=(16,8))
ax.scatter(data['year'], data['mass'])
plt.show()

Equator or Poles

In [19]:
axes = plt.gca()
axes.set_ylim([-90,90])
above_equator = data[data.lat >0].shape[0]
at_equator = data[data.lat ==0].shape[0]
below_equator = data[data.lat <0].shape[0]

print("Above Equator:", above_equator, '\n')
print("At Equator:", at_equator, '\n')
print("Below Equator:", below_equator, '\n')

labels = ["Above", 'At', 'Below']
values = [above_equator, at_equator, below_equator]
plt.pie(values, labels=labels)
plt.show()

Above Equator: 830 

At Equator: 623 

Below Equator: 2340
In [ ]: