Avocado

Bar plots are great for revealing relationships between categorical (size) and numeric (number sold) variables, but manipulation on the data first is needed in order to get the numbers needed for plotting.
Published

October 19, 2020

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
avocados = pd.read_csv('../dataset/avocado.csv', index_col=0)
avocados.head()
date type year avg_price size nb_sold
0 2015-12-27 conventional 2015 0.95 small 9626901.09
1 2015-12-20 conventional 2015 0.98 small 8710021.76
2 2015-12-13 conventional 2015 0.93 small 9855053.66
3 2015-12-06 conventional 2015 0.89 small 9405464.36
4 2015-11-29 conventional 2015 0.99 small 8094803.56 
avocados.tail()
date type year avg_price size nb_sold
1009 2018-02-04 organic 2018 1.53 extra_large 1703.52
1010 2018-01-28 organic 2018 1.61 extra_large 1270.61
1011 2018-01-21 organic 2018 1.63 extra_large 1490.02
1012 2018-01-14 organic 2018 1.59 extra_large 1580.01
1013 2018-01-07 organic 2018 1.51 extra_large 1289.07

Total number sold for each avocado size group

nb_sold_by_size = avocados.groupby('size')['nb_sold'].sum()
nb_sold_by_size.plot(kind='bar', x='size', y='nb_sold')
plt.show()

Bedazzling bar plot! It looks like small avocados were the most-purchased size, but large avocados were a close second.

Changes in sales over time

visualizing the change in avocado sales over three years.

Line plots are designed to visualize the relationship between two numeric variables, where each data values is connected to the next one. They are especially useful for visualizing change in a number over time, since each time point is naturally connected to the next time point. ### the total number of avocados sold on each date

nb_sold_by_date = avocados.groupby('date')['nb_sold'].sum()
nb_sold_by_date.head()
date
2015-01-04    27279606.03
2015-01-11    25081927.33
2015-01-18    24961540.48
2015-01-25    24094678.66
2015-02-01    39838734.08
Name: nb_sold, dtype: float64
nb_sold_by_date.plot(kind='line', x='date', y='nb_sold')
plt.show()

Here, it looks like the number of avocados spikes around the same time each year.

Avocado supply and demand

comparing the number of avocados sold to average price and see if they’re at all related. If they’re related, one number may be used to predict the other.

avocados.plot(kind='scatter', x='nb_sold', y='avg_price', title='Number of Avocados sold Vs. Average price')
plt.show()

It looks like when more avocados are sold, prices go down. However, this doesn’t mean that fewer sales causes higher prices - we can only tell that they’re correlated with each other.

Price of conventional vs. organic avocados

avocados[avocados['type']=='conventional']['avg_price'].hist(bins=20, alpha=0.5)
avocados[avocados['type']=='organic']['avg_price'].hist(bins=20, alpha=0.5)
plt.legend(['conventional', 'organic'])
plt.title='Price of Conventional vs. organic avocados'
plt.show()

We can see that on average, organic avocados are more expensive than conventional ones, but their price distributions have some overlap.

Missing Values

avocados_2016 = pd.read_csv('../dataset/avocados_2016.csv')
avocados_2016.head()
Unnamed: 0 date avg_price total_sold small_sold large_sold xl_sold total_bags_sold small_bags_sold large_bags_sold xl_bags_sold
0 0 2016-12-25 1.00 30287853.70 9255125.20 10282925.61 541972.42 10207830.47 7709584.33 2417144.92 81101.22
1 1 2016-12-18 0.96 29583882.61 9394065.91 10339168.20 427872.42 9422776.08 6970320.34 2358443.96 94011.78
2 2 2016-12-11 0.98 30093540.70 9009996.11 NaN 403047.93 10713276.64 8149438.75 2490495.07 73342.82
3 3 2016-12-04 1.00 31621221.90 11043350.90 9908982.97 428009.84 10240878.19 7187022.58 2988504.98 65350.63
4 4 2016-11-27 1.21 22923062.65 7891487.94 7337341.77 NaN 7349757.04 5691266.56 1609867.20 48623.28 
avocados_2016.tail()
Unnamed: 0 date avg_price total_sold small_sold large_sold xl_sold total_bags_sold small_bags_sold large_bags_sold xl_bags_sold
47 47 2016-01-31 0.93 34721249.92 11098088.49 13278053.61 1643703.17 8701404.65 6684515.27 1823889.58 192999.80
48 48 2016-01-24 0.95 32787079.21 7020459.00 14054367.50 1440380.36 10271872.35 7438489.60 2668368.01 165014.74
49 49 2016-01-17 0.94 34426341.87 10036597.84 13832256.04 1419405.62 9138082.37 6846324.06 2228420.91 63337.40
50 50 2016-01-10 0.93 35264336.01 NaN 13226088.66 1282612.91 9314347.55 6735384.32 2528089.14 50874.09
51 51 2016-01-03 0.86 38142088.04 NaN 16054083.86 1560068.62 8911429.39 6464895.83 2287449.76 159083.80 
avocados_2016.isna().any()
Unnamed: 0         False
date               False
avg_price          False
total_sold         False
small_sold          True
large_sold          True
xl_sold             True
total_bags_sold    False
small_bags_sold    False
large_bags_sold    False
xl_bags_sold       False
dtype: bool
avocados_2016.isna().sum().plot(kind='bar')
plt.show()

It looks like there are missing values in the small_sold, large_sold, and xl_sold columns.

Removing missing values

every row containing missing values will be deleted

avocados_complete = avocados_2016.dropna()
avocados_complete.isna().any()
Unnamed: 0         False
date               False
avg_price          False
total_sold         False
small_sold         False
large_sold         False
xl_sold            False
total_bags_sold    False
small_bags_sold    False
large_bags_sold    False
xl_bags_sold       False
dtype: bool
avocados_2016[["small_sold", "large_sold", "xl_sold"]].hist()
plt.show()