<jupyter_start><jupyter_text>Lambda School Data Science - A First Look at Data  Lecture - let's explore Python DS libraries and examples!The Python Data Science ecosystem is huge. You've seen some of the big pieces - pandas, scikit-learn, matplotlib. What parts do you want to see more of?<jupyter_code># TODO - we'll be doing this live, taking requests
#test
# and reproducing what it is to look up and learn things<jupyter_output><empty_output><jupyter_text>Assignment - now it's your turnPick at least one Python DS library, and using documentation/examples reproduce in this notebook something cool. It's OK if you don't fully understand it or get it 100% working, but do put in effort and look things up.<jupyter_code># TODO - your code here

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

df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/airline-safety/airline-safety.csv')

print(df.head())
print(df.isnull().sum())

histogram1 = plt.hist(df['fatalities_85_99'], color = 'b')
plt.show()

histogram2 = plt.hist(df['fatalities_00_14'], color = 'r')
plt.show()






# Use what we did live in lecture as an example<jupyter_output>airline  ...  fatalities_00_14
0             Aer Lingus  ...                 0
1              Aeroflot*  ...                88
2  Aerolineas Argentinas  ...                 0
3            Aeromexico*  ...                 0
4             Air Canada  ...                 0

[5 rows x 8 columns]
airline                   0
avail_seat_km_per_week    0
incidents_85_99           0
fatal_accidents_85_99     0
fatalities_85_99          0
incidents_00_14           0
fatal_accidents_00_14     0
fatalities_00_14          0
dtype: int64
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