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ium_434742/laby-inz-um.ipynb
patrycjalazna 2c992734bf tasks from IUM_02.Dane.ipynb
2021-03-21 16:49:08 +01:00

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OPIS

Dataset zawiera dane dotyczące cen awokado Hass i ich sprzedaży w wybranych regionach Stanów Zjednoczonych.

Opis kolumn:

  • Date - data obserwacji
  • AveragePrice - średnia cena pojedynczego awokado
  • type - zwykłe lub organiczne
  • year - rok obserwacji
  • Region - miasto/region obserwacji
  • Total Volume - liczba sprzedanych awokado
  • 4046 - liczba sprzedanych awokado z kodem PLU 4046 (małe)
  • 4225 - liczba sprzedanych awokado z kodem PLU 4225 (duże)
  • 4770 - liczba sprzedanych awokado z kodem PLU 4770 (bardzo duże)
import sys
!{sys.executable} -m pip install kaggle
!echo OOOOOOOOO {sys.executable}
!{sys.executable} -m pip install pandas
!python3 -m pip install sklearn
Requirement already satisfied: kaggle in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (1.5.12)
Requirement already satisfied: six>=1.10 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from kaggle) (1.15.0)
Requirement already satisfied: requests in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from kaggle) (2.25.1)
Requirement already satisfied: python-dateutil in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from kaggle) (2.8.1)
Requirement already satisfied: python-slugify in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from kaggle) (4.0.1)
Requirement already satisfied: urllib3 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from kaggle) (1.26.2)
Requirement already satisfied: tqdm in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from kaggle) (4.59.0)
Requirement already satisfied: certifi in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from kaggle) (2020.12.5)
Requirement already satisfied: text-unidecode>=1.3 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from python-slugify->kaggle) (1.3)
Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from requests->kaggle) (4.0.0)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from requests->kaggle) (2.10)
OOOOOOOOO /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/bin/python
Requirement already satisfied: pandas in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (1.2.3)
Requirement already satisfied: numpy>=1.16.5 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from pandas) (1.20.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from pandas) (2.8.1)
Requirement already satisfied: pytz>=2017.3 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from pandas) (2020.4)
Requirement already satisfied: six>=1.5 in /usr/local/Cellar/jupyterlab/3.0.0_1/libexec/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)
Requirement already satisfied: sklearn in /usr/local/lib/python3.9/site-packages (0.0)
Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/site-packages (from sklearn) (0.24.1)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (1.0.1)
Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (1.6.1)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (2.1.0)
Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (1.20.1)

Pobranie zbioru.

!kaggle datasets download -d timmate/avocado-prices-2020
!unzip -o avocado-prices-2020.zip

Archive:  avocado-prices-2020.zip
  inflating: avocado-updated-2020.csv  

!head -n 5 avocado-updated-2020.csv
date,average_price,total_volume,4046,4225,4770,total_bags,small_bags,large_bags,xlarge_bags,type,year,geography
2015-01-04,1.22,40873.28,2819.5,28287.42,49.9,9716.46,9186.93,529.53,0.0,conventional,2015,Albany
2015-01-04,1.79,1373.95,57.42,153.88,0.0,1162.65,1162.65,0.0,0.0,organic,2015,Albany
2015-01-04,1.0,435021.49,364302.39,23821.16,82.15,46815.79,16707.15,30108.64,0.0,conventional,2015,Atlanta
2015-01-04,1.76,3846.69,1500.15,938.35,0.0,1408.19,1071.35,336.84,0.0,organic,2015,Atlanta

Usunięcie zbędnej kolumny (redundantne dane).

import pandas as pd
avocado_with_year = pd.read_csv('avocado-updated-2020.csv')
avocado_with_year

new = ['date', 'average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags', 'type', 'geography']
avocado = avocado_with_year[new]
avocado.to_csv("avocado.csv", index=False)
avocado = pd.read_csv('avocado.csv')
avocado
date average_price total_volume 4046 4225 4770 total_bags small_bags large_bags xlarge_bags type geography
0 2015-01-04 1.22 40873.28 2819.50 28287.42 49.90 9716.46 9186.93 529.53 0.00 conventional Albany
1 2015-01-04 1.79 1373.95 57.42 153.88 0.00 1162.65 1162.65 0.00 0.00 organic Albany
2 2015-01-04 1.00 435021.49 364302.39 23821.16 82.15 46815.79 16707.15 30108.64 0.00 conventional Atlanta
3 2015-01-04 1.76 3846.69 1500.15 938.35 0.00 1408.19 1071.35 336.84 0.00 organic Atlanta
4 2015-01-04 1.08 788025.06 53987.31 552906.04 39995.03 141136.68 137146.07 3990.61 0.00 conventional Baltimore/Washington
... ... ... ... ... ... ... ... ... ... ... ... ...
33040 2020-11-29 1.47 1583056.27 67544.48 97996.46 2617.17 1414878.10 906711.52 480191.83 27974.75 organic Total U.S.
33041 2020-11-29 0.91 5811114.22 1352877.53 589061.83 19741.90 3790665.29 2197611.02 1531530.14 61524.13 conventional West
33042 2020-11-29 1.48 289961.27 13273.75 19341.09 636.51 256709.92 122606.21 134103.71 0.00 organic West
33043 2020-11-29 0.67 822818.75 234688.01 80205.15 10543.63 497381.96 285764.11 210808.02 809.83 conventional West Tex/New Mexico
33044 2020-11-29 1.35 24106.58 1236.96 617.80 1564.98 20686.84 17824.52 2862.32 0.00 organic West Tex/New Mexico

33045 rows × 12 columns

Podział zbioru na train/dev/test.

import numpy as np

avocado_train, avocado_validate, avocado_test = np.split(avocado.sample(frac=1), [int(.6*len(avocado)), int(.8*len(avocado))])

Podsumowanie zbioru i poszczególnych podzbiorów.

Wielkości zbioru i podzbiorów.

print("Avocado: ".ljust(20), np.size(avocado))
print("Avocado (train) : ".ljust(20), np.size(avocado_train))
print("Avocado (validate): ".ljust(20), np.size(avocado_validate))
print("Avocado (test) ".ljust(20), np.size(avocado_test))
Avocado:             396540
Avocado (train) :    237924
Avocado (validate):  79308
Avocado (test)       79308

Podsumowanie zbioru avocado.

avocado.describe(include = 'all')
date average_price total_volume 4046 4225 4770 total_bags small_bags large_bags xlarge_bags type geography
count 33045 33045.000000 3.304500e+04 3.304500e+04 3.304500e+04 3.304500e+04 3.304500e+04 3.304500e+04 3.304500e+04 3.304500e+04 33045 33045
unique 306 NaN NaN NaN NaN NaN NaN NaN NaN NaN 2 54
top 2017-10-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN conventional Atlanta
freq 108 NaN NaN NaN NaN NaN NaN NaN NaN NaN 16524 612
mean NaN 1.379941 9.683997e+05 3.023914e+05 2.797693e+05 2.148255e+04 3.646735e+05 2.501980e+05 1.067329e+05 7.742585e+03 NaN NaN
std NaN 0.378972 3.934533e+06 1.301026e+06 1.151052e+06 1.001607e+05 1.564004e+06 1.037734e+06 5.167226e+05 4.819803e+04 NaN NaN
min NaN 0.440000 8.456000e+01 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 NaN NaN
25% NaN 1.100000 1.511895e+04 7.673100e+02 2.712470e+03 0.000000e+00 9.121860e+03 6.478630e+03 4.662900e+02 0.000000e+00 NaN NaN
50% NaN 1.350000 1.291170e+05 1.099477e+04 2.343600e+04 1.780900e+02 5.322224e+04 3.687699e+04 6.375860e+03 0.000000e+00 NaN NaN
75% NaN 1.620000 5.058285e+05 1.190219e+05 1.352389e+05 5.096530e+03 1.744314e+05 1.206624e+05 4.041723e+04 8.044400e+02 NaN NaN
max NaN 3.250000 6.371614e+07 2.274362e+07 2.047057e+07 2.546439e+06 3.168919e+07 2.055041e+07 1.332760e+07 1.403184e+06 NaN NaN

Podsumowanie podzbioru train.

avocado_train.describe(include= 'all' )
date average_price total_volume 4046 4225 4770 total_bags small_bags large_bags xlarge_bags type geography
count 19827 19827.000000 1.982700e+04 1.982700e+04 1.982700e+04 1.982700e+04 1.982700e+04 1.982700e+04 1.982700e+04 1.982700e+04 19827 19827
unique 306 NaN NaN NaN NaN NaN NaN NaN NaN NaN 2 54
top 2018-09-23 NaN NaN NaN NaN NaN NaN NaN NaN NaN organic Sacramento
freq 77 NaN NaN NaN NaN NaN NaN NaN NaN NaN 9954 404
mean NaN 1.380658 9.503549e+05 2.955048e+05 2.762023e+05 2.117442e+04 3.573659e+05 2.448356e+05 1.049736e+05 7.556707e+03 NaN NaN
std NaN 0.377988 3.896388e+06 1.285945e+06 1.147780e+06 1.008332e+05 1.548676e+06 1.023617e+06 5.161354e+05 4.776408e+04 NaN NaN
min NaN 0.460000 2.534500e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 NaN NaN
25% NaN 1.100000 1.509891e+04 7.560400e+02 2.695640e+03 0.000000e+00 9.095285e+03 6.430960e+03 4.678750e+02 0.000000e+00 NaN NaN
50% NaN 1.350000 1.275485e+05 1.086294e+04 2.337789e+04 1.714100e+02 5.240743e+04 3.663295e+04 6.148990e+03 0.000000e+00 NaN NaN
75% NaN 1.610000 4.996119e+05 1.174216e+05 1.337254e+05 4.976950e+03 1.721448e+05 1.193927e+05 3.875767e+04 7.391950e+02 NaN NaN
max NaN 3.170000 6.371614e+07 2.113740e+07 2.047057e+07 2.546439e+06 3.168919e+07 2.055041e+07 1.332760e+07 1.403184e+06 NaN NaN

Podsumowanie podzbioru validate.

avocado_validate.describe(include = 'all')
date average_price total_volume 4046 4225 4770 total_bags small_bags large_bags xlarge_bags type geography
count 6609 6609.000000 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6609 6609
unique 306 NaN NaN NaN NaN NaN NaN NaN NaN NaN 2 54
top 2020-05-03 NaN NaN NaN NaN NaN NaN NaN NaN NaN organic Jacksonville
freq 35 NaN NaN NaN NaN NaN NaN NaN NaN NaN 3365 149
mean NaN 1.382624 9.914296e+05 3.140144e+05 2.827458e+05 2.172480e+04 3.729031e+05 2.567059e+05 1.085372e+05 7.660065e+03 NaN NaN
std NaN 0.380997 4.042527e+06 1.341419e+06 1.181393e+06 1.021178e+05 1.596924e+06 1.065783e+06 5.196275e+05 4.795256e+04 NaN NaN
min NaN 0.440000 8.456000e+01 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 NaN NaN
25% NaN 1.100000 1.486299e+04 7.570000e+02 2.534810e+03 0.000000e+00 9.007310e+03 6.281480e+03 4.562400e+02 0.000000e+00 NaN NaN
50% NaN 1.350000 1.241199e+05 1.023778e+04 2.204006e+04 1.674700e+02 5.247009e+04 3.492217e+04 6.458780e+03 0.000000e+00 NaN NaN
75% NaN 1.620000 5.026773e+05 1.207824e+05 1.307007e+05 5.104000e+03 1.706264e+05 1.197749e+05 4.128634e+04 7.951300e+02 NaN NaN
max NaN 3.250000 6.250565e+07 2.274362e+07 2.044550e+07 1.800066e+06 2.666884e+07 1.740824e+07 1.077854e+07 1.123540e+06 NaN NaN

Podsumowanie podzbioru test.

avocado_test.describe(include = 'all')
date average_price total_volume 4046 4225 4770 total_bags small_bags large_bags xlarge_bags type geography
count 6609 6609.000000 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6.609000e+03 6609 6609
unique 306 NaN NaN NaN NaN NaN NaN NaN NaN NaN 2 54
top 2020-06-21 NaN NaN NaN NaN NaN NaN NaN NaN NaN conventional California
freq 33 NaN NaN NaN NaN NaN NaN NaN NaN NaN 3407 143
mean NaN 1.375107 9.995041e+05 3.114282e+05 2.874940e+05 2.216469e+04 3.783667e+05 2.597775e+05 1.102065e+05 8.382739e+03 NaN NaN
std NaN 0.379902 3.939225e+06 1.305043e+06 1.130053e+06 9.608845e+04 1.576553e+06 1.051335e+06 5.156234e+05 4.971697e+04 NaN NaN
min NaN 0.480000 3.855500e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 NaN NaN
25% NaN 1.090000 1.544873e+04 8.225900e+02 2.903380e+03 0.000000e+00 9.358110e+03 6.834760e+03 4.706000e+02 0.000000e+00 NaN NaN
50% NaN 1.330000 1.409398e+05 1.233835e+04 2.530639e+04 2.074500e+02 5.576654e+04 3.897502e+04 7.182140e+03 0.000000e+00 NaN NaN
75% NaN 1.610000 5.330085e+05 1.221341e+05 1.453971e+05 5.358790e+03 1.833669e+05 1.254250e+05 4.531138e+04 1.012940e+03 NaN NaN
max NaN 3.000000 5.453235e+07 1.707665e+07 1.789639e+07 1.993645e+06 2.735245e+07 1.791382e+07 1.063102e+07 1.181516e+06 NaN NaN

Rozkład częstości przykładów dla poszczególnych klas.

avocado.geography.value_counts() 
Atlanta                 612
St. Louis               612
New York                612
Indianapolis            612
Sacramento              612
Spokane                 612
Philadelphia            612
South Carolina          612
West                    612
San Francisco           612
Orlando                 612
Southeast               612
Miami/Ft. Lauderdale    612
Nashville               612
Syracuse                612
Columbus                612
Detroit                 612
Northern New England    612
Buffalo/Rochester       612
Raleigh/Greensboro      612
Midsouth                612
Boise                   612
San Diego               612
Hartford/Springfield    612
Los Angeles             612
Total U.S.              612
Dallas/Ft. Worth        612
Great Lakes             612
Roanoke                 612
Plains                  612
California              612
Portland                612
Grand Rapids            612
Harrisburg/Scranton     612
Charlotte               612
Cincinnati/Dayton       612
Richmond/Norfolk        612
Houston                 612
South Central           612
Northeast               612
Seattle                 612
Jacksonville            612
Baltimore/Washington    612
Pittsburgh              612
Louisville              612
Boston                  612
Tampa                   612
Phoenix/Tucson          612
Chicago                 612
Denver                  612
Las Vegas               612
Albany                  612
New Orleans/Mobile      612
West Tex/New Mexico     609
Name: geography, dtype: int64
avocado_test.geography.value_counts() 
California              143
Grand Rapids            139
Roanoke                 139
Las Vegas               139
Spokane                 137
Plains                  135
Seattle                 134
Louisville              132
Atlanta                 131
Syracuse                130
New York                130
Nashville               129
Raleigh/Greensboro      129
Miami/Ft. Lauderdale    128
Phoenix/Tucson          128
Orlando                 128
Hartford/Springfield    127
San Francisco           127
South Central           127
Charlotte               126
Richmond/Norfolk        126
West                    126
Tampa                   124
Los Angeles             124
South Carolina          122
Great Lakes             122
Total U.S.              122
Northeast               121
Cincinnati/Dayton       121
Columbus                121
Baltimore/Washington    119
Pittsburgh              119
Jacksonville            119
Portland                119
West Tex/New Mexico     118
Midsouth                118
Houston                 117
Chicago                 116
Buffalo/Rochester       116
New Orleans/Mobile      116
Philadelphia            115
San Diego               115
Indianapolis            115
Northern New England    114
Boston                  114
Boise                   114
Southeast               114
Dallas/Ft. Worth        113
Detroit                 113
Albany                  112
Denver                  111
St. Louis               111
Harrisburg/Scranton     104
Sacramento              100
Name: geography, dtype: int64
avocado_train.geography.value_counts() 
Sacramento              404
Albany                  398
Northern New England    390
Harrisburg/Scranton     388
St. Louis               385
Columbus                384
Boise                   382
Indianapolis            381
Detroit                 380
South Carolina          378
West Tex/New Mexico     378
Southeast               378
Nashville               377
Denver                  377
Los Angeles             377
Great Lakes             376
San Diego               375
Cincinnati/Dayton       374
Boston                  374
South Central           373
New Orleans/Mobile      373
Richmond/Norfolk        371
Seattle                 371
Total U.S.              371
Buffalo/Rochester       370
Northeast               369
Charlotte               368
Atlanta                 368
Chicago                 367
San Francisco           366
Midsouth                366
Philadelphia            365
New York                363
Portland                363
Syracuse                362
Grand Rapids            361
Louisville              361
Roanoke                 361
Dallas/Ft. Worth        360
Orlando                 359
Tampa                   359
Houston                 359
Hartford/Springfield    358
Pittsburgh              357
West                    356
Miami/Ft. Lauderdale    354
Baltimore/Washington    353
Phoenix/Tucson          353
Raleigh/Greensboro      345
Jacksonville            344
Las Vegas               339
California              336
Plains                  335
Spokane                 335
Name: geography, dtype: int64
pd.value_counts(avocado['type']).plot.bar()
<AxesSubplot:>
pd.value_counts(avocado_train['type']).plot.bar()
<AxesSubplot:>
pd.value_counts(avocado_test['type']).plot.bar()
<AxesSubplot:>
avocado['average_price'].hist()
avocado_train['average_price'].hist()
avocado_validate['average_price'].hist()
avocado_test['average_price'].hist()
<AxesSubplot:>

Normalizacja wartości.

# według https://www.journaldev.com/45109/normalize-data-in-python
from sklearn import preprocessing

num_values = avocado.select_dtypes(include='float64').values
scaler = preprocessing.MinMaxScaler()
x_scaled = scaler.fit_transform(num_values)
num_columns = avocado.select_dtypes(include='float64').columns
avocado_normalized = pd.DataFrame(x_scaled, columns=num_columns)
for col in avocado.columns:
    if col in num_columns: 
        avocado[col] = avocado_normalized[col]
        
avocado
date average_price total_volume 4046 4225 4770 total_bags small_bags large_bags xlarge_bags type geography
0 2015-01-04 0.277580 0.000640 0.000124 0.001382 0.000020 0.000307 0.000447 0.000040 0.000000 conventional Albany
1 2015-01-04 0.480427 0.000020 0.000003 0.000008 0.000000 0.000037 0.000057 0.000000 0.000000 organic Albany
2 2015-01-04 0.199288 0.006826 0.016018 0.001164 0.000032 0.001477 0.000813 0.002259 0.000000 conventional Atlanta
3 2015-01-04 0.469751 0.000059 0.000066 0.000046 0.000000 0.000044 0.000052 0.000025 0.000000 organic Atlanta
4 2015-01-04 0.227758 0.012366 0.002374 0.027010 0.015706 0.004454 0.006674 0.000299 0.000000 conventional Baltimore/Washington
... ... ... ... ... ... ... ... ... ... ... ... ...
33040 2020-11-29 0.366548 0.024844 0.002970 0.004787 0.001028 0.044649 0.044121 0.036030 0.019937 organic Total U.S.
33041 2020-11-29 0.167260 0.091202 0.059484 0.028776 0.007753 0.119620 0.106938 0.114914 0.043846 conventional West
33042 2020-11-29 0.370107 0.004550 0.000584 0.000945 0.000250 0.008101 0.005966 0.010062 0.000000 organic West
33043 2020-11-29 0.081851 0.012913 0.010319 0.003918 0.004141 0.015696 0.013906 0.015817 0.000577 conventional West Tex/New Mexico
33044 2020-11-29 0.323843 0.000377 0.000054 0.000030 0.000615 0.000653 0.000867 0.000215 0.000000 organic West Tex/New Mexico

33045 rows × 12 columns

Usunięcie artefaktów.

avocado.isnull().sum()
date             0
average_price    0
total_volume     0
4046             0
4225             0
4770             0
total_bags       0
small_bags       0
large_bags       0
xlarge_bags      0
type             0
geography        0
dtype: int64
avocado.dropna()
date average_price total_volume 4046 4225 4770 total_bags small_bags large_bags xlarge_bags type geography
0 2015-01-04 0.277580 0.000640 0.000124 0.001382 0.000020 0.000307 0.000447 0.000040 0.000000 conventional Albany
1 2015-01-04 0.480427 0.000020 0.000003 0.000008 0.000000 0.000037 0.000057 0.000000 0.000000 organic Albany
2 2015-01-04 0.199288 0.006826 0.016018 0.001164 0.000032 0.001477 0.000813 0.002259 0.000000 conventional Atlanta
3 2015-01-04 0.469751 0.000059 0.000066 0.000046 0.000000 0.000044 0.000052 0.000025 0.000000 organic Atlanta
4 2015-01-04 0.227758 0.012366 0.002374 0.027010 0.015706 0.004454 0.006674 0.000299 0.000000 conventional Baltimore/Washington
... ... ... ... ... ... ... ... ... ... ... ... ...
33040 2020-11-29 0.366548 0.024844 0.002970 0.004787 0.001028 0.044649 0.044121 0.036030 0.019937 organic Total U.S.
33041 2020-11-29 0.167260 0.091202 0.059484 0.028776 0.007753 0.119620 0.106938 0.114914 0.043846 conventional West
33042 2020-11-29 0.370107 0.004550 0.000584 0.000945 0.000250 0.008101 0.005966 0.010062 0.000000 organic West
33043 2020-11-29 0.081851 0.012913 0.010319 0.003918 0.004141 0.015696 0.013906 0.015817 0.000577 conventional West Tex/New Mexico
33044 2020-11-29 0.323843 0.000377 0.000054 0.000030 0.000615 0.000653 0.000867 0.000215 0.000000 organic West Tex/New Mexico

33045 rows × 12 columns