Exploring with Visuals

Use clean_08.csv and clean_18.csv. You should've created these data files in the previous section: Fixing Data Types Pt 3.

In [1]:
# load datasets
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

df_08 = pd.read_csv('clean_08.csv')
df_18 = pd.read_csv('clean_18.csv')
In [2]:
len(df_08[df_08['greenhouse_gas_score'] == 9])
Out[2]:
10
In [3]:
df_08.greenhouse_gas_score.hist()
Out[3]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f4a62fcdf28>
In [4]:
df_18.greenhouse_gas_score.hist()
Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f4a8c0d4860>
In [5]:
df_08[["city_mpg", "hwy_mpg", "cmb_mpg"]].hist(figsize=(8,8))
Out[5]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a8c149d68>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a60e6a710>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a60e22710>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a60dda630>]], dtype=object)
In [6]:
df_18[["city_mpg", "hwy_mpg", "cmb_mpg"]].hist(figsize=(8,8))
Out[6]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a60b85828>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a60bbe2e8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a60d3c358>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a60cf5358>]], dtype=object)

Describe the correlation between displacement and combined mpg.

Negative Correlation - below code supports my conclusion

In [11]:
df_08.groupby(['cmb_mpg', 'displ']).mean()
Out[11]:
cyl air_pollution_score city_mpg hwy_mpg greenhouse_gas_score
cmb_mpg displ
10.0 6.5 12.000000 6.000000 8.000000 13.000000 0.000000
8.0 16.000000 6.000000 8.000000 14.000000 0.000000
11.0 5.7 12.000000 6.000000 9.000000 16.000000 0.000000
6.5 12.000000 6.000000 9.000000 14.000000 0.000000
6.7 8.000000 6.000000 9.333333 15.000000 0.000000
12.0 5.0 10.000000 6.000000 10.000000 15.500000 0.000000
5.4 8.000000 6.000000 11.000000 13.000000 0.000000
5.5 12.000000 6.000000 10.000000 16.000000 0.000000
5.7 12.000000 6.000000 10.000000 15.000000 0.000000
5.9 12.000000 6.000000 10.500000 15.500000 0.000000
6.0 12.000000 6.000000 10.000000 16.500000 0.000000
6.1 8.000000 6.000000 11.000000 14.000000 0.000000
13.0 5.0 9.333333 6.000000 11.000000 16.333333 1.000000
5.4 8.000000 6.666667 11.333333 15.333333 1.000000
5.5 12.000000 6.000000 11.000000 17.000000 1.000000
5.9 12.000000 6.000000 11.000000 17.500000 1.000000
6.0 10.000000 6.000000 11.250000 16.500000 1.000000
6.2 8.000000 6.000000 11.000000 17.000000 1.000000
14.0 4.2 8.000000 6.000000 12.000000 17.800000 2.000000
4.4 8.000000 6.500000 12.000000 17.500000 2.000000
4.5 8.000000 6.000000 13.000000 17.000000 2.000000
4.6 8.000000 6.000000 13.000000 17.000000 2.000000
4.7 8.000000 6.250000 12.750000 16.750000 2.000000
4.8 8.000000 6.000000 12.000000 19.000000 2.000000
5.0 9.714286 6.000000 12.000000 18.142857 2.000000
5.3 8.000000 6.000000 12.000000 16.000000 2.000000
5.4 8.000000 6.857143 12.428571 17.142857 2.000000
5.5 10.000000 6.500000 12.000000 17.500000 2.000000
5.6 8.000000 6.000000 12.000000 17.428571 2.000000
5.7 8.000000 6.500000 12.750000 17.250000 2.000000
... ... ... ... ... ... ...
25.0 2.4 4.000000 6.777778 21.722222 30.500000 7.000000
3.0 6.000000 7.666667 23.000000 31.000000 7.000000
3.3 6.000000 9.000000 26.500000 24.000000 7.000000
26.0 1.6 4.000000 6.000000 22.000000 31.000000 8.000000
2.0 4.000000 7.250000 23.000000 30.750000 8.000000
2.2 4.000000 7.500000 22.000000 31.000000 8.000000
2.4 4.000000 6.000000 22.000000 32.000000 8.000000
2.5 4.000000 7.333333 23.111111 31.333333 8.000000
3.3 6.000000 9.000000 27.000000 25.000000 8.000000
27.0 1.6 4.000000 6.000000 24.000000 31.000000 8.000000
1.8 4.000000 7.000000 24.666667 31.333333 8.000000
2.0 4.000000 7.800000 24.000000 32.000000 8.000000
2.2 4.000000 7.500000 24.000000 33.000000 8.000000
2.4 4.000000 6.000000 24.000000 32.000000 8.000000
28.0 1.6 4.000000 6.500000 24.500000 33.000000 8.000000
1.8 4.000000 7.333333 25.333333 33.000000 8.333333
2.0 4.000000 8.200000 24.600000 33.400000 8.000000
2.3 4.000000 9.000000 29.000000 27.000000 8.000000
29.0 1.5 4.000000 6.000000 27.000000 33.000000 8.000000
1.6 4.000000 6.750000 26.250000 33.500000 8.000000
1.8 4.000000 6.857143 26.142857 33.857143 8.000000
30.0 1.5 4.000000 6.000000 27.000000 34.000000 8.000000
31.0 1.5 4.000000 6.500000 28.500000 34.500000 9.000000
1.8 4.000000 7.000000 28.000000 37.000000 9.000000
32.0 1.5 4.000000 7.000000 29.000000 36.000000 9.000000
2.3 4.000000 9.000000 34.000000 30.000000 9.000000
34.0 2.4 4.000000 9.000000 33.000000 34.000000 9.000000
2.5 4.000000 9.000000 35.000000 33.000000 9.000000
42.0 1.3 4.000000 9.000000 40.000000 45.000000 10.000000
46.0 1.5 4.000000 9.000000 48.000000 45.000000 10.000000

212 rows × 5 columns

Describe the correlation between greenhouse gas score and combined mpg.

Positive Correlation - below code concludes my answer

In [14]:
df_08.groupby(['cmb_mpg', 'greenhouse_gas_score']).mean()
Out[14]:
displ cyl air_pollution_score city_mpg hwy_mpg
cmb_mpg greenhouse_gas_score
10.0 0 7.000000 13.333333 6.000000 8.000000 13.333333
11.0 0 6.471429 10.285714 6.000000 9.142857 14.714286
12.0 0 5.720000 11.200000 6.000000 10.266667 15.533333
13.0 1 5.606250 10.000000 6.125000 11.125000 16.500000
14.0 2 5.346875 8.312500 6.171875 12.140625 17.406250
15.0 3 4.936585 7.829268 6.378049 13.292683 18.414634
7 3.000000 6.000000 6.000000 13.000000 19.000000
16.0 4 4.452830 7.094340 6.415094 13.943396 19.754717
17.0 4 3.972131 6.360656 6.475410 14.983607 20.327869
18.0 5 3.755844 6.311688 6.402597 15.571429 22.311688
19.0 5 3.457303 6.022472 6.561798 16.438202 23.730337
20.0 6 3.354255 5.893617 6.510638 17.351064 24.542553
21.0 6 2.839286 5.142857 6.535714 18.464286 25.369048
22.0 6 2.725743 4.900990 6.831683 19.356436 26.821782
23.0 7 2.489831 4.762712 7.186441 20.135593 27.576271
24.0 7 2.401493 4.507463 7.313433 21.089552 29.000000
25.0 7 2.458333 4.444444 7.055556 22.250000 29.972222
26.0 8 2.325000 4.083333 7.208333 22.791667 30.958333
27.0 8 2.058824 4.000000 7.235294 24.117647 32.176471
28.0 8 1.975000 4.000000 7.833333 25.916667 31.416667
9 1.800000 4.000000 9.000000 24.000000 36.000000
29.0 8 1.708333 4.000000 6.750000 26.250000 33.666667
30.0 8 1.500000 4.000000 6.000000 27.000000 34.000000
31.0 9 1.600000 4.000000 6.666667 28.333333 35.333333
32.0 9 2.100000 4.000000 8.500000 32.750000 31.500000
34.0 9 2.450000 4.000000 9.000000 34.000000 33.500000
42.0 10 1.300000 4.000000 9.000000 40.000000 45.000000
46.0 10 1.500000 4.000000 9.000000 48.000000 45.000000
In [ ]: