In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

In [2]:

earthquakes = pd.read_csv('all_month.csv')
earthquakes.head()

Out[2]:

	time	latitude	longitude	depth	mag	magType	nst	gap	dmin	rms	...	updated	place	type	horizontalError	depthError	magError	magNst	status	locationSource	magSource
0	2021-11-30T12:18:16.030Z	38.833000	-122.805000	1.97	1.44	md	31.0	50.0	0.01038	0.02	...	2021-11-30T12:21:30.201Z	7km W of Cobb, CA	earthquake	0.17	0.31	0.13	7.0	automatic	nc	nc
1	2021-11-30T12:17:42.360Z	38.834835	-122.805168	1.98	0.22	md	13.0	61.0	0.01205	0.01	...	2021-11-30T12:19:17.907Z	7km WNW of Cobb, CA	earthquake	0.32	0.72	0.14	2.0	automatic	nc	nc
2	2021-11-30T11:50:23.770Z	19.332834	-155.099503	1.63	1.87	md	30.0	164.0	NaN	0.30	...	2021-11-30T11:53:40.990Z	15 km SSE of Fern Forest, Hawaii	earthquake	0.63	0.64	0.94	12.0	automatic	hv	hv
3	2021-11-30T11:37:13.280Z	17.979500	-67.004000	10.00	2.70	md	20.0	155.0	0.04150	0.15	...	2021-11-30T11:52:24.629Z	4 km E of La Parguera, Puerto Rico	earthquake	0.45	0.59	0.16	10.0	reviewed	pr	pr
4	2021-11-30T11:33:05.650Z	38.835167	-122.805168	1.78	0.85	md	10.0	77.0	0.01202	0.01	...	2021-11-30T11:34:42.006Z	7km WNW of Cobb, CA	earthquake	0.32	1.04	NaN	1.0	automatic	nc	nc

5 rows × 22 columns

Notes: Using groupby on Type (Earthquake, Explosions, Ice quake, Quarry Blast?), groupby Location then place, Magnitudes bigger than or equal to 5. Questions: Which locations are hit by (event) most frequently? What time (window) do the events most often occur? What is relationship between time of day and an event occuring? Are there any predictors/strong relationships/indicators that an event is going to occur? Average magnitude per event compared?

In [3]:

earthquakes.describe()

Out[3]:

	latitude	longitude	depth	mag	nst	gap	dmin	rms	horizontalError	depthError	magError	magNst
count	9134.000000	9134.000000	9134.000000	9133.000000	6564.000000	7580.000000	6370.000000	9133.000000	6740.000000	9133.000000	6993.000000	7577.000000
mean	36.529624	-112.178530	22.755205	1.675445	21.284126	116.696895	0.660005	0.275026	1.676261	2.276648	0.327450	14.108618
std	19.612662	63.009157	53.944145	1.221161	15.674889	58.743783	2.292870	0.288512	3.008785	9.457025	0.601628	26.664414
min	-62.624300	-179.967300	-7.300000	-1.210000	0.000000	12.000000	0.000000	0.000000	0.080000	0.000000	0.000000	0.000000
25%	33.318542	-149.782175	3.152500	0.850000	10.000000	72.000000	0.019095	0.090000	0.280000	0.470000	0.110000	4.000000
50%	38.168950	-121.142751	8.305000	1.480000	16.000000	103.000000	0.063889	0.150000	0.490000	0.780000	0.170000	8.000000
75%	44.980667	-116.000975	19.200000	2.100000	28.000000	151.000000	0.175900	0.397600	0.935134	1.570000	0.264000	15.000000
max	85.862200	179.941000	632.600000	7.500000	221.000000	354.680000	35.610000	2.910000	20.600000	751.600000	5.470000	593.000000

In [4]:

earthquakes.isnull().sum()

Out[4]:

time                  0
latitude              0
longitude             0
depth                 0
mag                   1
magType               1
nst                2570
gap                1554
dmin               2764
rms                   1
net                   0
id                    0
updated               0
place                 0
type                  0
horizontalError    2394
depthError            1
magError           2141
magNst             1557
status                0
locationSource        0
magSource             0
dtype: int64

In [5]:

fg, ax = plt.subplots()
earthquakes.boxplot(column='mag', ax=ax)

Out[5]:

<AxesSubplot:>

In [6]:

earthquakes.groupby(['type']).count()

Out[6]:

	time	latitude	longitude	depth	mag	magType	nst	gap	dmin	rms	...	id	updated	place	horizontalError	depthError	magError	magNst	status	locationSource	magSource
type
chemical explosion	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
earthquake	8947	8947	8947	8947	8946	8946	6411	7428	6217	8947	...	8947	8947	8947	6608	8947	6842	7424	8947	8947	8947
explosion	49	49	49	49	49	49	41	41	41	49	...	49	49	49	41	49	41	41	49	49	49
ice quake	26	26	26	26	26	26	0	0	0	26	...	26	26	26	0	26	0	0	26	26	26
other event	1	1	1	1	1	1	1	1	1	1	...	1	1	1	1	1	1	1	1	1	1
quarry blast	109	109	109	109	109	109	109	109	109	109	...	109	109	109	89	109	108	109	109	109	109
sonic boom	1	1	1	1	1	1	1	0	1	0	...	1	1	1	0	0	0	1	1	1	1

7 rows × 21 columns

In [7]:

earthquakes.groupby(['type']).sum()['mag']

Out[7]:

type
chemical explosion        1.770000
earthquake            15035.670002
explosion                74.070000
ice quake                37.300000
other event               1.560000
quarry blast            151.470000
sonic boom                0.000000
Name: mag, dtype: float64

In [8]:

earthquakes.groupby(['type']).count()['mag']

Out[8]:

type
chemical explosion       1
earthquake            8946
explosion               49
ice quake               26
other event              1
quarry blast           109
sonic boom               1
Name: mag, dtype: int64

In [9]:

averageMag = earthquakes.groupby(['type']).sum()['mag']/earthquakes.groupby(['type']).count()['mag']
averageMag

Out[9]:

type
chemical explosion    1.770000
earthquake            1.680714
explosion             1.511633
ice quake             1.434615
other event           1.560000
quarry blast          1.389633
sonic boom            0.000000
Name: mag, dtype: float64

In [10]:

averageMag = averageMag.to_frame('Average Magnitude')
averageMag.reset_index(inplace=True)
averageMag

Out[10]:

	type	Average Magnitude
0	chemical explosion	1.770000
1	earthquake	1.680714
2	explosion	1.511633
3	ice quake	1.434615
4	other event	1.560000
5	quarry blast	1.389633
6	sonic boom	0.000000

In [11]:

fig1, ax = plt.subplots()
averageMag.plot(kind='bar', x='type', y='Average Magnitude', ax = ax, color=['orange','brown','red','blue','green','gray'], title='Average Magnitude Per Event', ylabel='Average Magnitude', xlabel='Event Type', legend=False)
plt.tight_layout()

In [12]:

fig1.savefig('Figure1_AvMag.jpg')

In [13]:

Magnitude = earthquakes.loc[:,['mag']]
Magnitude

Out[13]:

	mag
0	1.44
1	0.22
2	1.87
3	2.70
4	0.85
...	...
9129	0.34
9130	0.65
9131	2.29
9132	4.90
9133	-0.01

9134 rows × 1 columns

In [ ]:

mag_list = Magnitude.values.tolist()
print(sum(mag_list, []))

In [16]:

Mag = (sum(mag_list, []))
print(Mag)

[1.44, 0.22, 1.87, 2.7, 0.85, 1.29, 4.3, 0.37, 5.0, 0.36, 4.9, 5.0, 1.3, 0.82, 5.0, 1.8, 6.0, 0.4, 2.78, 1.1, 1.69, 1.3, 0.83, 2.1, 0.8, 0.36, 2.08, 2.1, 2.18, 0.9, 1.92, 1.01, 2.1, 2.86, 1.7, 1.26, 1.9, 1.4, 0.29, 1.0, 2.4000001, 2.4, 2.2, 2.17, 1.19, 1.0, 1.0, 2.1, 2.57, 0.61, 5.5, 0.7, 0.81, 1.54, 0.69, 4.5, 1.4, 2.27999997, 0.98, 0.48, 0.49, 0.73, 0.37, 0.37, 0.3, 0.86, 0.82, 1.3, 1.94, 1.11, 0.98, 0.93, 5.0, 0.6, 0.87, 1.78, 0.88, 2.7, 4.9, 1.46, 0.86, 2.08, 2.27999997, 0.36, 2.51, 2.1, 0.66, 3.16, 0.87, 0.86, 0.84, 0.23, 1.95000005, 0.35, 0.86, 1.5, 1.86000001, 0.75, 0.73, 4.6, 0.6, 2.76, 2.57, 0.4, 0.86, 0.49, 1.53, 4.3, 2.49, 5.1, 1.55, 1.63, 4.6, 0.36, 2.51, 1.86000001, 1.3, 2.84, 1.91, 4.4, 0.98, 0.68, 1.3, 2.57, 1.90999997, 0.37, 1.6, 2.08999991, 4.7, 0.36, 1.57, 1.79, 4.6, 0.9, 0.76, 0.18, 2.4, 2.00999999, 1.8, 4.4, 1.5, 1.13, 0.7, 0.53, 2.21, 1.7, 0.21, 2.85, 0.21, 0.33, 4.9, 1.88999999, 4.6, 4.6, 2.0, 2.41, 1.21, 4.7, 0.46, 2.1, 1.23, 1.8, 1.85, 2.0999999, 0.85, 2.9, 1.83000004, 5.4, 0.97, 1.1, 1.0, 2.1500001, 1.6, 1.25, 1.7, 2.5, 1.5, 2.21, 3.06, 1.18, 0.8, 1.42, 1.88, 4.6, 2.5, 1.3, 0.38, 0.71, 0.85, 1.71000004, 0.9, 0.92, 1.0, 1.62, 4.6, 1.4, 3.7, 1.39, 2.0, 1.18, 0.61, 1.92, 2.64, 2.08, 1.78, 2.21, 4.7, 2.1, 2.3, 1.1, 2.19, 0.89, 2.5, 1.9, 0.18, 0.17, 0.36, 1.3, 6.3, -0.48, 1.1, 1.12, 1.23, 3.22, 1.85000002, 1.8, 2.0, 1.03, 1.34, 0.87, 1.7, 4.0, 1.3, 0.96, 0.9, 0.2, 1.45, 2.33, 1.6, 0.92, 1.6, 0.36, 0.43, 0.66, 0.95, 0.92, 1.86000001, 0.78, 1.3, 1.6, 1.6, 1.4, 0.6, 2.31, 1.6, 5.1, 2.61, 4.2, 2.4, 1.5, 0.86, 1.70000005, 1.39, 1.89, 0.86, 3.6, 4.4, 1.43, 0.9, 2.25, 1.55, 0.49, 1.6, 1.4, 0.38, 0.73, 2.1400001, 2.0, 1.03, 3.51, 0.94, 2.2, 4.3, 2.18, 4.5, 1.25, 2.0, 1.04, 4.3, 0.9, 1.5, 1.03, 1.0, 2.47, 1.1, 1.1, 1.0, 0.98, 1.55, 1.4, 1.88, 1.5, 1.93, 0.85, 1.4, 1.6, 1.1, 2.69, 2.1, 0.42, 2.34, 0.55, 0.62, 2.31999993, 2.03, 0.97, 2.1, 1.83000004, 1.83000004, 1.97, 0.85, 4.2, 4.1, 1.91, 4.3, 2.61, 2.44, 2.3, 1.58, 1.3, 1.03, 2.25, -0.26, 0.89, 2.4, 0.6, 2.05999994, 2.5, 0.98, 1.45, 1.1, 1.6, 1.8, 0.74, 1.16, 2.3, 1.7, 1.35, 1.3, -0.63, 1.2, 2.37, 1.6, 1.58, 1.71, 1.7, 1.2, 1.7, 2.41, 1.7, 1.0, 1.0, 1.3, 1.3, 0.46, 1.44, 2.27, 3.3, 1.42, 2.7, 0.54, 3.1, 0.4, 4.3, 0.41, 1.1, 4.6, 1.91999996, 1.0, 4.1, -1.0, 0.56, 0.46, 2.21000004, 2.44, 1.2, 2.32, -0.04, 1.12, 1.7, 1.7, 0.35, 0.59, 1.89, -0.59, 4.6, 4.7, 2.89, 2.5, 1.02, 1.73, 2.6, 1.11, 1.46, 0.86, 1.64, 1.71, 0.86, 1.93, 2.5, 0.36, 0.47, 3.12, 1.8, 2.20000005, 2.0, 3.1, 1.19, 0.6, 2.32, 2.38, 0.7, 4.5, 1.17, 1.31, 1.9, 4.5, 1.80999994, 0.8, 4.3, 1.87, 1.88, 1.2, 1.7, 1.24, 3.28, 0.8, 1.0, 2.97, 3.6, 4.2, 0.8, 0.5, 3.56, 1.19, 0.7, 0.67, 1.0, 1.5, 1.27, 0.8, 0.9, 2.77, 1.59, 4.5, 1.7, -0.47, 2.00999999, 1.7, 0.28, 1.74000001, 4.9, 1.7, 2.1, 1.68, 2.9, 0.59, 1.17, 0.57, 0.93, 1.9, 2.2, 0.21, 1.19, 0.36, 1.97, 2.2, 1.7, 1.92999995, 1.01, 2.33, 1.87, 1.2, 1.13, 0.58, 0.4, 1.75999999, 1.9, 1.51, 0.61, 3.07, 1.75999999, 2.03999996, 0.86, 2.71, 7.5, 1.58, 0.54, 1.04, 1.35, 0.6, 0.99, 1.0, 0.9, 1.4, 0.17, 1.78999996, 0.44, 0.58, 1.9, 1.8, 1.42, 1.9, 0.5, 4.5, 0.36, 0.84, 0.67, 1.8, 4.0, 0.62, -0.05, 0.78, 0.84, 0.88, 2.08, 1.48, 0.19, 2.24000001, 1.57, 4.2, 1.69, 2.9, 2.71, 2.44, -0.48, 2.30999994, 1.35, 0.7, 0.49, 2.63, 0.68, 2.2, 2.7, 1.4, 0.37, 2.04999995, 2.9, 1.99, 2.74, 0.75, 2.38, 1.4, 1.0, 0.85, 0.9, 4.1, 0.73, 1.7, 3.0, 5.1, 2.9, 1.1, 2.29, 3.2, 1.14, 0.6, 1.25, 0.72, 2.8, 3.2, 1.27, 0.58, 0.73, 1.87, 2.04999995, 1.56, 1.37, 2.25999999, 1.61, 2.4000001, 1.33, 1.32, 0.37, 1.22, 1.63, 1.7, 0.9, 1.2, 0.85, 1.9, 0.67, 0.85, 2.0, 0.41, 3.0, 1.06, 1.41, 3.25, 0.95, 1.6, 0.63, 3.46, 1.34, 4.4, 3.33, 1.34, 2.11, 1.5, 1.2, 2.26, 1.7, 0.99, 2.3, 0.86, 0.79, 0.95, 3.04, 1.2, 2.8, 0.58, 4.3, 3.7, 1.15, 4.7, 1.4, 4.6, 2.79, 2.3, 1.6, 1.7, 0.8, 1.44, -1.0, 2.74, 1.44, 0.84, 4.1, 0.6, 1.15, 0.73, 1.99, 1.85000002, 1.91999996, 1.88999999, 2.0, 0.82, 1.76999998, 0.9, 3.0, 0.96, 0.32, 1.37, 1.5, -0.92, 1.2, 1.4, 2.54, 1.1, 0.8, 1.2, 0.91, 4.6, 1.6, 1.4, 1.5, 4.9, 4.8, 2.41, 0.36, 0.55, 0.89, -0.78, 0.6, 2.1, 1.7, 1.21, 0.35, 1.31, 0.28, 0.9, 1.24, 2.65, 1.4, 0.47, 1.84000003, 1.2, 1.35, 4.1, 2.4, 5.3, 1.3, 0.59, 1.74, 0.66, 1.78, 0.21, 1.86000001, 0.87, 4.3, 0.86, 4.4, -0.21, 4.6, 1.5, 1.8, 2.2, 2.24000001, 1.6, 0.32, 0.54, 1.99, 5.3, 4.6, 0.74, 1.7, 2.02, 1.7, 1.1, 0.05, 4.6, 0.85, 1.1, 1.88999999, 2.44, 0.7, 2.3, 1.06, 0.8, 3.0, 0.83, 1.08, 2.1, 1.4, 3.02, 3.27, 1.5, 1.84, 0.75, 0.72, 0.18, 1.76, 1.85000002, 0.6, 0.43, 0.47, 0.75, 0.84, 1.8, 2.93, 2.4, 1.8, 1.0, 0.98, 0.81, 3.2, 0.96, 0.65, 1.07, 1.6, 1.82, 1.16, 0.89, 0.93, 1.19, 1.59, 0.86, 1.4, 1.85, 0.85, 1.89, 0.56, 2.01999998, 0.74, 2.57, 0.77, 1.77, 1.04, 1.32, 1.6, 2.3, 1.25, 3.48, 0.4, 2.46, 0.8, -0.23, 0.61, 1.04, 2.8, 1.88, 2.01, 2.1, 1.0, 1.9, 1.88, 0.26, 0.66, 0.56, 0.41, 0.64, 1.7, 0.78, 0.3, 0.18, 0.74, 0.9, 1.6, 0.63, -0.23, -0.38, 2.16, 3.29, 0.9, 1.73, 1.94, 2.76, 3.46, 1.7, 1.8, 1.57, 0.8, 2.6, 0.87, 1.5, 4.9, 0.85, 2.12, 1.85000002, 2.17000008, 1.2, 0.74, 2.16000009, 1.90999997, 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-0.9, 1.49, 2.14, 2.22, 1.5, 1.9, 0.37, 1.54, 0.92, 2.26, 0.83, 0.55, 0.04, 1.61, 1.46, 1.79999995, 1.14, 2.84, 0.72, 0.11, 2.09, 1.01, 2.38, 1.56, 0.8, 1.55, 1.87, 2.1, 2.39, 1.16, 4.4, 0.8, 1.09, 1.68, 0.52, 0.9, 1.03, 0.4, 2.25999999, 2.14, 2.0, 1.64, 3.27, 0.62, 1.69, 1.5, 4.5, 1.41, 1.73, 1.99, 0.85, 0.56, 1.71, -0.55, 1.46, 4.4, 3.8, 0.95, 0.7, 0.95, 1.51, 0.52, 0.72, 0.17, 2.3, 1.98, 2.82, 2.44, 0.81, 1.06, 0.48, 0.41, 0.36, 1.31, 1.44, 0.77, 1.86000001, 1.55, 2.33, 5.0, 1.45, 0.72, 2.1, 0.83, 1.18, 3.09, 1.6, 2.2, 2.03999996, 4.5, 0.49, 1.47, 0.81, 1.73000002, 4.8, 1.6, 0.36, 1.42, 1.42, 4.0, 1.85000002, 5.0, 0.64, 1.8, 0.16, 4.7, 5.0, 1.9, 2.23, 0.8, 1.5, -0.55, 0.5, 2.01999998, 1.24, 1.91, 4.3, 3.2, 0.67, 1.46, 1.82000005, 2.57, 4.0, 1.0, 4.3, 2.94, 1.53, 0.76, 1.52, 1.9, 0.81, 0.85, 0.8, 1.87, 4.3, -0.1, 4.9, 1.58, 2.19000006, 0.8, 1.07, 1.64, 0.6, 6.0, 5.1, 1.14, 4.4, 0.85, 4.4, 1.52, 1.46, 4.2, 1.33, 1.85000002, 1.67, 1.0, 3.06, 2.0, 1.97, 0.49, 2.52, 2.1, 2.01999998, 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In [62]:

mag_ranges = pd.cut(Mag, 9, right=False, labels=['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G', 'Group H', 'Group I'], retbins=True, precision=3, include_lowest=False, ordered=True)
mag_ranges

Out[62]:

(['Group C', 'Group B', 'Group D', 'Group E', 'Group C', ..., 'Group B', 'Group B', 'Group D', 'Group G', 'Group B']
 Length: 9134
 Categories (9, object): ['Group A' < 'Group B' < 'Group C' < 'Group D' ... 'Group F' < 'Group G' < 'Group H' < 'Group I'],
 array([-1.21      , -0.24222222,  0.72555556,  1.69333333,  2.66111111,
         3.62888889,  4.59666667,  5.56444444,  6.53222222,  7.50871   ]))

In [63]:

#labels = ['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G', 'Group H', 'Group I']
ranges = [-1.5, -0.5,  0.5,  1.5,  2.5,
         3.5,  4.5,  5.5,  6.5,  8.0]
MagRange = pd.DataFrame(earthquakes['mag'].groupby(pd.cut(earthquakes.mag, ranges)).count())
MagRange

Out[63]:

	mag
mag
(-1.5, -0.5]	68
(-0.5, 0.5]	1079
(0.5, 1.5]	3628
(1.5, 2.5]	2895
(2.5, 3.5]	506
(3.5, 4.5]	573
(4.5, 5.5]	354
(5.5, 6.5]	28
(6.5, 8.0]	2

In [74]:

fig2, ax = plt.subplots()

MagPie = MagRange.plot.pie(y='mag', ax=ax, labels=['A','B','C','D','E','F','G','H','I'], autopct='%1.2f%%', figsize=(20,15))
ax.set_title('Magnitude Ranges')
ax.legend(labels=['Group A: -1.49 to -0.5', 'Group B: -0.49 to 0.5', 'Group C: 0.51 to 1.5', 'Group D: 1.51 to 2.5', 'Group E: 2.51 to 3.5', 'Group F: 3.51 to 4.5', 'Group G: 4.51 to 5.5', 'Group H: 5.51 to 6.5', 'Group I: 6.51 to 8.0'], loc='upper left')
ax.yaxis.label.set_color('white')
plt.tight_layout()


#'Group A: -1.49 to -0.5', 'Group B: -0.49 to 0.5', 'Group C: 0.51 to 1.5', 'Group D: 1.51 to 2.5', 'Group E: 2.51 to 3.5', 'Group F: 3.51 to 4.5', 'Group G: 4.51 to 5.5', 'Group H: 5.51 to 6.5', 'Group I: 6.51 to 8.0'
#magnitudes = pd.DataFrame({'ranges': [-1.21, -0.24222222, 0.72555556, 1.69333333, 2.66111111, 3.62888889, 4.59666667, 5.56444444, 6.53222222, 7.50871]},
                  #index=[, 'Group J'])
#plot = magnitudes.plot.pie(subplots=True, figsize=(5, 5))

In [76]:

fig2.savefig('Figure2_MagRange.jpg')

In [99]:

dfDepth = earthquakes[['depth']]
dfDepth

Out[99]:

	depth
0	1.97
1	1.98
2	1.63
3	10.00
4	1.78
...	...
9129	4.44
9130	3.47
9131	8.37
9132	121.09
9133	4.29

9134 rows × 1 columns

In [106]:

dfDepth2 = pd.DataFrame(dfDepth, columns=['earthquake', 'chemical explosion', 'explosion', 'ice quake', 'other event', 'quarry blast', 'sonic boom'])
dfDepth2

Out[106]:

	earthquake	chemical explosion	explosion	ice quake	other event	quarry blast	sonic boom
0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...
9129	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9130	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9131	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9132	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9133	NaN	NaN	NaN	NaN	NaN	NaN	NaN

9134 rows × 7 columns

In [107]:

fig3, ax = plt.subplots()
dfDepth2.boxplot(column=['earthquake', 'chemical explosion', 'explosion', 'ice quake', 'other event', 'quarry blast', 'sonic boom'], ax=ax)

Out[107]:

<AxesSubplot:>

In [ ]: