来源：Kaggle 官方课程 Pandas
2024-08-30@isSeymour

Pandas - Kaggle

1. Creating, Reading and Writing

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import pandas as pd
pd.set_option('display.max_rows', 5)
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fruits = pd.DataFrame({'Apples': [30],
                       'Bananas': [21]})
fruits
Apples Bananas
0 30 21 
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fruit_sales = pd.DataFrame({'Apples': [35, 41],
                            'Bananas': [21, 34]},
                          index=['2017 Sales', '2018 Sales'])
fruit_sales
Apples Bananas
2017 Sales 35 21
2018 Sales 41 34 
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ingredients = pd.Series(['4 cups', '1 cup', '2 large', '1 can'],
                       index=['Flour', 'Milk', 'Eggs', 'Spam'], name='Dinner')
ingredients
Flour     4 cups
Milk       1 cup
Eggs     2 large
Spam       1 can
Name: Dinner, dtype: object

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reviews = pd.read_csv("../input/winemag-data_first150k.csv",index_col=0)
reviews
country description designation points price province region_1 region_2 variety winery
0 US This tremendous 100% varietal wine hails from ... Martha's Vineyard 96 235.0 California Napa Valley Napa Cabernet Sauvignon Heitz
1 Spain Ripe aromas of fig, blackberry and cassis are ... Carodorum Selección Especial Reserva 96 110.0 Northern Spain Toro NaN Tinta de Toro Bodega Carmen Rodríguez
... ... ... ... ... ... ... ... ... ... ...
150928 France A perfect salmon shade, with scents of peaches... Grand Brut Rosé 90 52.0 Champagne Champagne NaN Champagne Blend Gosset
150929 Italy More Pinot Grigios should taste like this. A r... NaN 90 15.0 Northeastern Italy Alto Adige NaN Pinot Grigio Alois Lageder

150930 rows × 10 columns

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animals = pd.DataFrame({'Cows': [12, 20], 'Goats': [22, 19]}, index=['Year 1', 'Year 2'])
animals.to_csv("cows_and_goats.csv")

2. Indexing, Selecting & Assigning

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reviews = pd.read_csv("../input/winemag-data-130k-v2.csv", index_col=0)
reviews.head()
country description designation points price province region_1 region_2 taster_name taster_twitter_handle title variety winery
0 Italy Aromas include tropical fruit, broom, brimston... Vulkà Bianco 87 NaN Sicily & Sardinia Etna NaN Kerin O’Keefe @kerinokeefe Nicosia 2013 Vulkà Bianco (Etna) White Blend Nicosia
1 Portugal This is ripe and fruity, a wine that is smooth... Avidagos 87 15.0 Douro NaN NaN Roger Voss @vossroger Quinta dos Avidagos 2011 Avidagos Red (Douro) Portuguese Red Quinta dos Avidagos
2 US Tart and snappy, the flavors of lime flesh and... NaN 87 14.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Rainstorm 2013 Pinot Gris (Willamette Valley) Pinot Gris Rainstorm
3 US Pineapple rind, lemon pith and orange blossom ... Reserve Late Harvest 87 13.0 Michigan Lake Michigan Shore NaN Alexander Peartree NaN St. Julian 2013 Reserve Late Harvest Riesling ... Riesling St. Julian
4 US Much like the regular bottling from 2012, this... Vintner's Reserve Wild Child Block 87 65.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Sweet Cheeks 2012 Vintner's Reserve Wild Child... Pinot Noir Sweet Cheeks 
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desc = reviews.description
desc
0         Aromas include tropical fruit, broom, brimston...
1         This is ripe and fruity, a wine that is smooth...
                                ...                        
129969    A dry style of Pinot Gris, this is crisp with ...
129970    Big, rich and off-dry, this is powered by inte...
Name: description, Length: 129971, dtype: object

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type(desc)
pandas.core.series.Series

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first_description = reviews.description[0]
first_description
"Aromas include tropical fruit, broom, brimstone and dried herb. The palate isn't overly expressive, offering unripened apple, citrus and dried sage alongside brisk acidity."

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first_row = reviews.loc[0]
first_row
country                                                    Italy
description    Aromas include tropical fruit, broom, brimston...
                                     ...                        
variety                                              White Blend
winery                                                   Nicosia
Name: 0, Length: 13, dtype: object

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first_descriptions = reviews.loc[0:9, 'description']
first_descriptions
0    Aromas include tropical fruit, broom, brimston...
1    This is ripe and fruity, a wine that is smooth...
                           ...                        
8    Savory dried thyme notes accent sunnier flavor...
9    This has great depth of flavor with its fresh ...
Name: description, Length: 10, dtype: object

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sample_reviews = reviews.loc[[1,2,3,5,8]]
sample_reviews
country description designation points price province region_1 region_2 taster_name taster_twitter_handle title variety winery
1 Portugal This is ripe and fruity, a wine that is smooth... Avidagos 87 15.0 Douro NaN NaN Roger Voss @vossroger Quinta dos Avidagos 2011 Avidagos Red (Douro) Portuguese Red Quinta dos Avidagos
2 US Tart and snappy, the flavors of lime flesh and... NaN 87 14.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Rainstorm 2013 Pinot Gris (Willamette Valley) Pinot Gris Rainstorm
3 US Pineapple rind, lemon pith and orange blossom ... Reserve Late Harvest 87 13.0 Michigan Lake Michigan Shore NaN Alexander Peartree NaN St. Julian 2013 Reserve Late Harvest Riesling ... Riesling St. Julian
5 Spain Blackberry and raspberry aromas show a typical... Ars In Vitro 87 15.0 Northern Spain Navarra NaN Michael Schachner @wineschach Tandem 2011 Ars In Vitro Tempranillo-Merlot (N... Tempranillo-Merlot Tandem
8 Germany Savory dried thyme notes accent sunnier flavor... Shine 87 12.0 Rheinhessen NaN NaN Anna Lee C. Iijima NaN Heinz Eifel 2013 Shine Gewürztraminer (Rheinhe... Gewürztraminer Heinz Eifel 
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df = reviews.loc[[0,1,10,100], ['country', 'province', 'region_1', 'region_2']]
df
country province region_1 region_2
0 Italy Sicily & Sardinia Etna NaN
1 Portugal Douro NaN NaN
10 US California Napa Valley Napa
100 US New York Finger Lakes Finger Lakes 
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df = reviews.loc[0:99, ['country', 'variety']]
df
country variety
0 Italy White Blend
1 Portugal Portuguese Red
... ... ...
98 Italy Sangiovese
99 US Bordeaux-style Red Blend

100 rows × 2 columns

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italian_wines = reviews.loc[reviews.country == 'Italy']
italian_wines
country description designation points price province region_1 region_2 taster_name taster_twitter_handle title variety winery
0 Italy Aromas include tropical fruit, broom, brimston... Vulkà Bianco 87 NaN Sicily & Sardinia Etna NaN Kerin O’Keefe @kerinokeefe Nicosia 2013 Vulkà Bianco (Etna) White Blend Nicosia
6 Italy Here's a bright, informal red that opens with ... Belsito 87 16.0 Sicily & Sardinia Vittoria NaN Kerin O’Keefe @kerinokeefe Terre di Giurfo 2013 Belsito Frappato (Vittoria) Frappato Terre di Giurfo
... ... ... ... ... ... ... ... ... ... ... ... ... ...
129961 Italy Intense aromas of wild cherry, baking spice, t... NaN 90 30.0 Sicily & Sardinia Sicilia NaN Kerin O’Keefe @kerinokeefe COS 2013 Frappato (Sicilia) Frappato COS
129962 Italy Blackberry, cassis, grilled herb and toasted a... Sàgana Tenuta San Giacomo 90 40.0 Sicily & Sardinia Sicilia NaN Kerin O’Keefe @kerinokeefe Cusumano 2012 Sàgana Tenuta San Giacomo Nero d... Nero d'Avola Cusumano

19540 rows × 13 columns

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top_oceania_wines = reviews.loc[(reviews.points >= 95) & (reviews.country.isin(['Australia', 'New Zealand']))]
top_oceania_wines
country description designation points price province region_1 region_2 taster_name taster_twitter_handle title variety winery
345 Australia This wine contains some material over 100 year... Rare 100 350.0 Victoria Rutherglen NaN Joe Czerwinski @JoeCz Chambers Rosewood Vineyards NV Rare Muscat (Ru... Muscat Chambers Rosewood Vineyards
346 Australia This deep brown wine smells like a damp, mossy... Rare 98 350.0 Victoria Rutherglen NaN Joe Czerwinski @JoeCz Chambers Rosewood Vineyards NV Rare Muscadelle... Muscadelle Chambers Rosewood Vineyards
... ... ... ... ... ... ... ... ... ... ... ... ... ...
122507 New Zealand This blend of Cabernet Sauvignon (62.5%), Merl... SQM Gimblett Gravels Cabernets/Merlot 95 79.0 Hawke's Bay NaN NaN Joe Czerwinski @JoeCz Squawking Magpie 2014 SQM Gimblett Gravels Cab... Bordeaux-style Red Blend Squawking Magpie
122939 Australia Full-bodied and plush yet vibrant and imbued w... The Factor 98 125.0 South Australia Barossa Valley NaN Joe Czerwinski @JoeCz Torbreck 2013 The Factor Shiraz (Barossa Valley) Shiraz Torbreck

49 rows × 13 columns

3. Summary Functions and Maps

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reviews = pd.read_csv("../input/winemag-data-130k-v2.csv", index_col=0)
reviews.head()
country description designation points price province region_1 region_2 taster_name taster_twitter_handle title variety winery
0 Italy Aromas include tropical fruit, broom, brimston... Vulkà Bianco 87 NaN Sicily & Sardinia Etna NaN Kerin O’Keefe @kerinokeefe Nicosia 2013 Vulkà Bianco (Etna) White Blend Nicosia
1 Portugal This is ripe and fruity, a wine that is smooth... Avidagos 87 15.0 Douro NaN NaN Roger Voss @vossroger Quinta dos Avidagos 2011 Avidagos Red (Douro) Portuguese Red Quinta dos Avidagos
2 US Tart and snappy, the flavors of lime flesh and... NaN 87 14.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Rainstorm 2013 Pinot Gris (Willamette Valley) Pinot Gris Rainstorm
3 US Pineapple rind, lemon pith and orange blossom ... Reserve Late Harvest 87 13.0 Michigan Lake Michigan Shore NaN Alexander Peartree NaN St. Julian 2013 Reserve Late Harvest Riesling ... Riesling St. Julian
4 US Much like the regular bottling from 2012, this... Vintner's Reserve Wild Child Block 87 65.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Sweet Cheeks 2012 Vintner's Reserve Wild Child... Pinot Noir Sweet Cheeks 
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median_points = reviews.points.median()
mean_points = reviews.points.mean()
print(median_points, '\t', mean_points)
88.0 	 88.44713820775404

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countries = reviews.country.unique()
countries
array(['Italy', 'Portugal', 'US', 'Spain', 'France', 'Germany',
       'Argentina', 'Chile', 'Australia', 'Austria', 'South Africa',
       'New Zealand', 'Israel', 'Hungary', 'Greece', 'Romania', 'Mexico',
       'Canada', nan, 'Turkey', 'Czech Republic', 'Slovenia',
       'Luxembourg', 'Croatia', 'Georgia', 'Uruguay', 'England',
       'Lebanon', 'Serbia', 'Brazil', 'Moldova', 'Morocco', 'Peru',
       'India', 'Bulgaria', 'Cyprus', 'Armenia', 'Switzerland',
       'Bosnia and Herzegovina', 'Ukraine', 'Slovakia', 'Macedonia',
       'China', 'Egypt'], dtype=object)

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reviews_per_country = reviews.country.value_counts()
reviews_per_country
country
US        54504
France    22093
          ...  
China         1
Egypt         1
Name: count, Length: 43, dtype: int64

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# mean_price = reviews.price.mean()   # 必须拿出来单独算，否则 CPU 飞起
# centered_price = reviews.price.map(lambda x: x-mean_price)
centered_price = reviews.price - reviews.price.mean()
centered_price
0               NaN
1        -20.363389
            ...    
129969    -3.363389
129970   -14.363389
Name: price, Length: 129971, dtype: float64

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bargain_ratio = (reviews.points / reviews.price)
print(bargain_ratio)
print('-'*25)
bargain_max = bargain_ratio.max()
bargain_idxmax = bargain_ratio.idxmax()
print(bargain_max, '\t', bargain_idxmax)

print('-'*25)
bargain_wine = reviews.loc[bargain_idxmax, 'title']
print(bargain_wine)
0              NaN
1         5.800000
            ...   
129969    2.812500
129970    4.285714
Length: 129971, dtype: float64
-------------------------
21.5 	 64590
-------------------------
Bandit NV Merlot (California)

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n_tro = reviews.description.map(lambda desc: 'tropical' in desc).sum()
n_fru = reviews.description.map(lambda desc: 'fruity' in desc).sum()
descriptor_counts = pd.Series([n_tro, n_fru], ['tropical', 'fruity'])
descriptor_counts
tropical    3607
fruity      9090
dtype: int64

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def get_star(row):
    if row.country == 'Canada':
        return 3
    elif row.points >= 95:
        return 3
    elif row.points >= 85:
        return 2
    else:
        return 1

star_ratings = reviews.apply(get_star, axis='columns')   # 0=rows, 1=columns
print(star_ratings)
0         2
1         2
         ..
129969    2
129970    2
Length: 129971, dtype: int64

4. Grouping and Sorting

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reviews = pd.read_csv("../input/winemag-data-130k-v2.csv", index_col=0)
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reviews_written = reviews.groupby("taster_twitter_handle").taster_twitter_handle.count()
print(reviews_written)
# 也可以
print("-"*25)
reviews_written = reviews.groupby("taster_twitter_handle").size()
print(reviews_written)
taster_twitter_handle
@AnneInVino        3685
@JoeCz             5147
                   ... 
@winewchristina       6
@worldwineguys     1005
Name: taster_twitter_handle, Length: 15, dtype: int64
-------------------------
taster_twitter_handle
@AnneInVino        3685
@JoeCz             5147
                   ... 
@winewchristina       6
@worldwineguys     1005
Length: 15, dtype: int64

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best_rating_per_price = reviews.groupby('price').points.max()
print(best_rating_per_price)
price
4.0       86
5.0       87
          ..
2500.0    96
3300.0    88
Name: points, Length: 390, dtype: int64

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price_extremes = reviews.groupby('variety').price.agg(["min", "max"])
price_extremes
min max
variety
Abouriou 15.0 75.0
Agiorgitiko 10.0 66.0
... ... ...
Çalkarası 19.0 19.0
Žilavka 15.0 15.0

707 rows × 2 columns

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sorted_varieties = price_extremes.sort_values(by=["min", "max"], ascending=False)
sorted_varieties
min max
variety
Ramisco 495.0 495.0
Terrantez 236.0 236.0
... ... ...
Vital NaN NaN
Zelen NaN NaN

707 rows × 2 columns

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reviewer_mean_ratings = reviews.groupby('taster_name').points.mean()
print(reviewer_mean_ratings)
taster_name
Alexander Peartree    85.855422
Anna Lee C. Iijima    88.415629
                        ...    
Susan Kostrzewa       86.609217
Virginie Boone        89.213379
Name: points, Length: 19, dtype: float64

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reviewer_mean_ratings.describe()
count    19.000000
mean     88.233026
           ...    
75%      88.975256
max      90.562551
Name: points, Length: 8, dtype: float64

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country_variety_counts = reviews.groupby(['country', 'variety']).size()
print(country_variety_counts)
print('-'*25)
country_variety_counts = country_variety_counts.sort_values(ascending=False)
print(country_variety_counts)
country    variety           
Argentina  Barbera                 1
           Bonarda               105
                                ... 
Uruguay    Tempranillo-Tannat      1
           White Blend             1
Length: 1612, dtype: int64
-------------------------
country  variety           
US       Pinot Noir            9885
         Cabernet Sauvignon    7315
                               ... 
Mexico   Rosado                   1
Uruguay  White Blend              1
Length: 1612, dtype: int64

5. Data Types and Missing Values

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reviews = pd.read_csv("../input/winemag-data-130k-v2.csv", index_col=0)
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dtype = reviews.points.dtype
print(dtype)
int64

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point_strings = reviews.points.astype('str')
point_strings
0         87
1         87
          ..
129969    90
129970    90
Name: points, Length: 129971, dtype: object

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missing_prices = reviews.price.isnull()
print(missing_prices)
print('-'*25)
n_missing_prices = missing_prices.sum()
print(n_missing_prices)


# Solution:
missing_price_reviews = reviews[reviews.price.isnull()]
n_missing_prices = len(missing_price_reviews)
# Cute alternative solution: if we sum a boolean series, True is treated as 1 and False as 0
n_missing_prices = reviews.price.isnull().sum()
# or equivalently:
n_missing_prices = pd.isnull(reviews.price).sum()
0          True
1         False
          ...  
129969    False
129970    False
Name: price, Length: 129971, dtype: bool
-------------------------
8996

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reviews_region = reviews.region_1.fillna('Unknown')
print(reviews_region)
print('-'*25)
reviews_per_region = reviews_region.value_counts()
print(reviews_per_region)
print('-'*25)
reviews_per_region = reviews_per_region.sort_values(ascending=False)
print(reviews_per_region)
0            Etna
1         Unknown
           ...   
129969     Alsace
129970     Alsace
Name: region_1, Length: 129971, dtype: object
-------------------------
region_1
Unknown                    21247
Napa Valley                 4480
                           ...  
Vin Santo di Carmignano        1
Paestum                        1
Name: count, Length: 1230, dtype: int64
-------------------------
region_1
Unknown        21247
Napa Valley     4480
               ...  
Geelong            1
Paestum            1
Name: count, Length: 1230, dtype: int64

6. Renaming and Combining

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reviews = pd.read_csv("../input/winemag-data-130k-v2.csv", index_col=0)
reviews.head()
country description designation points price province region_1 region_2 taster_name taster_twitter_handle title variety winery
0 Italy Aromas include tropical fruit, broom, brimston... Vulkà Bianco 87 NaN Sicily & Sardinia Etna NaN Kerin O’Keefe @kerinokeefe Nicosia 2013 Vulkà Bianco (Etna) White Blend Nicosia
1 Portugal This is ripe and fruity, a wine that is smooth... Avidagos 87 15.0 Douro NaN NaN Roger Voss @vossroger Quinta dos Avidagos 2011 Avidagos Red (Douro) Portuguese Red Quinta dos Avidagos
2 US Tart and snappy, the flavors of lime flesh and... NaN 87 14.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Rainstorm 2013 Pinot Gris (Willamette Valley) Pinot Gris Rainstorm
3 US Pineapple rind, lemon pith and orange blossom ... Reserve Late Harvest 87 13.0 Michigan Lake Michigan Shore NaN Alexander Peartree NaN St. Julian 2013 Reserve Late Harvest Riesling ... Riesling St. Julian
4 US Much like the regular bottling from 2012, this... Vintner's Reserve Wild Child Block 87 65.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Sweet Cheeks 2012 Vintner's Reserve Wild Child... Pinot Noir Sweet Cheeks 
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renamed = reviews.rename(columns={'region_1': 'region', 'region_2': 'locale'})
renamed.head()
country description designation points price province region locale taster_name taster_twitter_handle title variety winery
0 Italy Aromas include tropical fruit, broom, brimston... Vulkà Bianco 87 NaN Sicily & Sardinia Etna NaN Kerin O’Keefe @kerinokeefe Nicosia 2013 Vulkà Bianco (Etna) White Blend Nicosia
1 Portugal This is ripe and fruity, a wine that is smooth... Avidagos 87 15.0 Douro NaN NaN Roger Voss @vossroger Quinta dos Avidagos 2011 Avidagos Red (Douro) Portuguese Red Quinta dos Avidagos
2 US Tart and snappy, the flavors of lime flesh and... NaN 87 14.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Rainstorm 2013 Pinot Gris (Willamette Valley) Pinot Gris Rainstorm
3 US Pineapple rind, lemon pith and orange blossom ... Reserve Late Harvest 87 13.0 Michigan Lake Michigan Shore NaN Alexander Peartree NaN St. Julian 2013 Reserve Late Harvest Riesling ... Riesling St. Julian
4 US Much like the regular bottling from 2012, this... Vintner's Reserve Wild Child Block 87 65.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Sweet Cheeks 2012 Vintner's Reserve Wild Child... Pinot Noir Sweet Cheeks 
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reindexed = reviews.rename_axis('wines', axis='rows')
reindexed.head()
country description designation points price province region_1 region_2 taster_name taster_twitter_handle title variety winery
wines
0 Italy Aromas include tropical fruit, broom, brimston... Vulkà Bianco 87 NaN Sicily & Sardinia Etna NaN Kerin O’Keefe @kerinokeefe Nicosia 2013 Vulkà Bianco (Etna) White Blend Nicosia
1 Portugal This is ripe and fruity, a wine that is smooth... Avidagos 87 15.0 Douro NaN NaN Roger Voss @vossroger Quinta dos Avidagos 2011 Avidagos Red (Douro) Portuguese Red Quinta dos Avidagos
2 US Tart and snappy, the flavors of lime flesh and... NaN 87 14.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Rainstorm 2013 Pinot Gris (Willamette Valley) Pinot Gris Rainstorm
3 US Pineapple rind, lemon pith and orange blossom ... Reserve Late Harvest 87 13.0 Michigan Lake Michigan Shore NaN Alexander Peartree NaN St. Julian 2013 Reserve Late Harvest Riesling ... Riesling St. Julian
4 US Much like the regular bottling from 2012, this... Vintner's Reserve Wild Child Block 87 65.0 Oregon Willamette Valley Willamette Valley Paul Gregutt @paulgwine Sweet Cheeks 2012 Vintner's Reserve Wild Child... Pinot Noir Sweet Cheeks 
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gaming_products = pd.read_csv("../input//gaming.csv")
gaming_products['subreddit'] = "r/gaming"
movie_products = pd.read_csv("../input/movies.csv")
movie_products['subreddit'] = "r/movies"
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combined_products = pd.concat([gaming_products, movie_products])
combined_products.head()
name category amazon_link total_mentions subreddit_mentions subreddit
0 BOOMco Halo Covenant Needler Blaster Toys & Games https://www.amazon.com/BOOMco-Halo-Covenant-Ne... 4.0 4 r/gaming
1 Raspberry PI 3 Model B 1.2GHz 64-bit quad-core... Electronics https://www.amazon.com/Raspberry-Model-A1-2GHz... 19.0 3 r/gaming
2 CanaKit 5V 2.5A Raspberry Pi 3 Power Supply / ... Electronics https://www.amazon.com/CanaKit-Raspberry-Suppl... 7.0 3 r/gaming
3 Panasonic K-KJ17MCA4BA Advanced Individual Cel... Electronics https://www.amazon.com/Panasonic-Advanced-Indi... 29.0 2 r/gaming
4 Mayflash GameCube Controller Adapter for Wii U... Electronics https://www.amazon.com/GameCube-Controller-Ada... 24.0 2 r/gaming 
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powerlifting_meets = pd.read_csv("../input/meets.csv")
powerlifting_competitors = pd.read_csv("../input/openpowerlifting.csv")
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powerlifting_1 = powerlifting_meets.set_index("MeetID")
powerlifting_2 = powerlifting_competitors.set_index("MeetID")
print(powerlifting_1)
print('-'*25)
print(powerlifting_2)

powerlifting_combined = powerlifting_1.join(powerlifting_2)
print('-'*25)
print(powerlifting_combined)
                   MeetPath Federation        Date MeetCountry MeetState  \
MeetID                                                                     
0            365strong/1601  365Strong  2016-10-29         USA        NC   
1            365strong/1602  365Strong  2016-11-19         USA        MO   
...                     ...        ...         ...         ...       ...   
8480    xpc/2016-pro-finals        XPC  2016-03-05         USA        OH   
8481        xpc/2017-finals        XPC  2017-03-03         USA        OH   

         MeetTown                                           MeetName  
MeetID                                                                
0       Charlotte  2016 Junior & Senior National Powerlifting Cha...  
1           Ozark                  Thanksgiving Powerlifting Classic  
...           ...                                                ...  
8480     Columbus                                2016 XPC PRO Finals  
8481     Columbus                                    2017 XPC Finals  

[8482 rows x 7 columns]
-------------------------
                    Name Sex   Equipment   Age   Division  BodyweightKg  \
MeetID                                                                    
0       Angie Belk Terry   F       Wraps  47.0  Mst 45-49         59.60   
0            Dawn Bogart   F  Single-ply  42.0  Mst 40-44         58.51   
...                  ...  ..         ...   ...        ...           ...   
8481      Jeff Bumanglag   M   Multi-ply   NaN      Elite        126.73   
8481       Shane Hammock   M   Multi-ply   NaN      Elite        129.46   

       WeightClassKg  Squat4Kg  BestSquatKg  Bench4Kg  BestBenchKg  \
MeetID                                                               
0                 60       NaN        47.63       NaN        20.41   
0                 60       NaN       142.88       NaN        95.25   
...              ...       ...          ...       ...          ...   
8481             140       NaN          NaN       NaN          NaN   
8481             140       NaN          NaN       NaN          NaN   

        Deadlift4Kg  BestDeadliftKg  TotalKg Place   Wilks  
MeetID                                                      
0               NaN           70.31   138.35     1  155.05  
0               NaN          163.29   401.42     1  456.38  
...             ...             ...      ...   ...     ...  
8481            NaN          320.00   320.00     3  181.85  
8481            NaN          362.50   362.50     2  205.18  

[386414 rows x 16 columns]
-------------------------
               MeetPath Federation        Date MeetCountry MeetState  \
MeetID                                                                 
0        365strong/1601  365Strong  2016-10-29         USA        NC   
0        365strong/1601  365Strong  2016-10-29         USA        NC   
...                 ...        ...         ...         ...       ...   
8481    xpc/2017-finals        XPC  2017-03-03         USA        OH   
8481    xpc/2017-finals        XPC  2017-03-03         USA        OH   

         MeetTown                                           MeetName  \
MeetID                                                                 
0       Charlotte  2016 Junior & Senior National Powerlifting Cha...   
0       Charlotte  2016 Junior & Senior National Powerlifting Cha...   
...           ...                                                ...   
8481     Columbus                                    2017 XPC Finals   
8481     Columbus                                    2017 XPC Finals   

                    Name Sex   Equipment  ...  WeightClassKg Squat4Kg  \
MeetID                                    ...                           
0       Angie Belk Terry   F       Wraps  ...             60      NaN   
0            Dawn Bogart   F  Single-ply  ...             60      NaN   
...                  ...  ..         ...  ...            ...      ...   
8481      Jeff Bumanglag   M   Multi-ply  ...            140      NaN   
8481       Shane Hammock   M   Multi-ply  ...            140      NaN   

        BestSquatKg Bench4Kg  BestBenchKg  Deadlift4Kg  BestDeadliftKg  \
MeetID                                                                   
0             47.63      NaN        20.41          NaN           70.31   
0            142.88      NaN        95.25          NaN          163.29   
...             ...      ...          ...          ...             ...   
8481            NaN      NaN          NaN          NaN          320.00   
8481            NaN      NaN          NaN          NaN          362.50   

        TotalKg  Place   Wilks  
MeetID                          
0        138.35      1  155.05  
0        401.42      1  456.38  
...         ...    ...     ...  
8481     320.00      3  181.85  
8481     362.50      2  205.18  

[386414 rows x 23 columns]