import pandas as pddf = pd.read_csv('https://data.nasa.gov/resource/gh4g-9sfh.csv')
df.head()
name
id
nametype
recclass
mass
fall
year
reclat
reclong
geolocation
0
Aachen
1
Valid
L5
21.0
Fell
1880-01-01T00:00:00.000
50.77500
6.08333
(50.775, 6.08333)
1
Aarhus
2
Valid
H6
720.0
Fell
1951-01-01T00:00:00.000
56.18333
10.23333
(56.18333, 10.23333)
2
Abee
6
Valid
EH4
107000.0
Fell
1952-01-01T00:00:00.000
54.21667
-113.00000
(54.21667, -113.0)
3
Acapulco
10
Valid
Acapulcoite
1914.0
Fell
1976-01-01T00:00:00.000
16.88333
-99.90000
(16.88333, -99.9)
4
Achiras
370
Valid
L6
780.0
Fell
1902-01-01T00:00:00.000
-33.16667
-64.95000
(-33.16667, -64.95)
df.tail()
name
id
nametype
recclass
mass
fall
year
reclat
reclong
geolocation
995
Tirupati
24009
Valid
H6
230.0
Fell
1934-01-01T00:00:00.000
13.63333
79.41667
(13.63333, 79.41667)
996
Tissint
54823
Valid
Martian (shergottite)
7000.0
Fell
2011-01-01T00:00:00.000
29.48195
-7.61123
(29.48195, -7.61123)
997
Tjabe
24011
Valid
H6
20000.0
Fell
1869-01-01T00:00:00.000
-7.08333
111.53333
(-7.08333, 111.53333)
998
Tjerebon
24012
Valid
L5
16500.0
Fell
1922-01-01T00:00:00.000
-6.66667
106.58333
(-6.66667, 106.58333)
999
Tomakovka
24019
Valid
LL6
600.0
Fell
1905-01-01T00:00:00.000
47.85000
34.76667
(47.85, 34.76667)
df.fall.unique()
array(['Fell', 'Found'], dtype=object)
df.dtypes
name object
id int64
nametype object
recclass object
mass float64
fall object
year object
reclat float64
reclong float64
geolocation object
dtype: object