#!/usr/bin/env python # coding: utf-8 # # Data Aggregation and Group Operations # In[ ]: import numpy as np import pandas as pd PREVIOUS_MAX_ROWS = pd.options.display.max_rows pd.options.display.max_rows = 20 np.random.seed(12345) import matplotlib.pyplot as plt plt.rc('figure', figsize=(10, 6)) np.set_printoptions(precision=4, suppress=True) # ## GroupBy Mechanics # In[ ]: df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'], 'key2' : ['one', 'two', 'one', 'two', 'one'], 'data1' : np.random.randn(5), 'data2' : np.random.randn(5)}) df # In[ ]: grouped = df['data1'].groupby(df['key1']) grouped # In[ ]: grouped.mean() # In[ ]: means = df['data1'].groupby([df['key1'], df['key2']]).mean() means # In[ ]: means.unstack() # In[ ]: states = np.array(['Ohio', 'California', 'California', 'Ohio', 'Ohio']) years = np.array([2005, 2005, 2006, 2005, 2006]) df['data1'].groupby([states, years]).mean() # In[ ]: df.groupby('key1').mean() df.groupby(['key1', 'key2']).mean() # In[ ]: df.groupby(['key1', 'key2']).size() # ### Iterating Over Groups # In[ ]: for name, group in df.groupby('key1'): print(name) print(group) # In[ ]: for (k1, k2), group in df.groupby(['key1', 'key2']): print((k1, k2)) print(group) # In[ ]: pieces = dict(list(df.groupby('key1'))) pieces['b'] # In[ ]: df.dtypes grouped = df.groupby(df.dtypes, axis=1) # In[ ]: for dtype, group in grouped: print(dtype) print(group) # ### Selecting a Column or Subset of Columns # df.groupby('key1')['data1'] # df.groupby('key1')[['data2']] # df['data1'].groupby(df['key1']) # df[['data2']].groupby(df['key1']) # In[ ]: df.groupby(['key1', 'key2'])[['data2']].mean() # In[ ]: s_grouped = df.groupby(['key1', 'key2'])['data2'] s_grouped s_grouped.mean() # ### Grouping with Dicts and Series # In[ ]: people = pd.DataFrame(np.random.randn(5, 5), columns=['a', 'b', 'c', 'd', 'e'], index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis']) people.iloc[2:3, [1, 2]] = np.nan # Add a few NA values people # In[ ]: mapping = {'a': 'red', 'b': 'red', 'c': 'blue', 'd': 'blue', 'e': 'red', 'f' : 'orange'} # In[ ]: by_column = people.groupby(mapping, axis=1) by_column.sum() # In[ ]: map_series = pd.Series(mapping) map_series people.groupby(map_series, axis=1).count() # ### Grouping with Functions # In[ ]: people.groupby(len).sum() # In[ ]: key_list = ['one', 'one', 'one', 'two', 'two'] people.groupby([len, key_list]).min() # ### Grouping by Index Levels # In[ ]: columns = pd.MultiIndex.from_arrays([['US', 'US', 'US', 'JP', 'JP'], [1, 3, 5, 1, 3]], names=['cty', 'tenor']) hier_df = pd.DataFrame(np.random.randn(4, 5), columns=columns) hier_df # In[ ]: hier_df.groupby(level='cty', axis=1).count() # ## Data Aggregation # In[ ]: df grouped = df.groupby('key1') grouped['data1'].quantile(0.9) # In[ ]: def peak_to_peak(arr): return arr.max() - arr.min() grouped.agg(peak_to_peak) # In[ ]: grouped.describe() # ### Column-Wise and Multiple Function Application # In[ ]: tips = pd.read_csv('examples/tips.csv') # Add tip percentage of total bill tips['tip_pct'] = tips['tip'] / tips['total_bill'] tips[:6] # In[ ]: grouped = tips.groupby(['day', 'smoker']) # In[ ]: grouped_pct = grouped['tip_pct'] grouped_pct.agg('mean') # In[ ]: grouped_pct.agg(['mean', 'std', peak_to_peak]) # In[ ]: grouped_pct.agg([('foo', 'mean'), ('bar', np.std)]) # In[ ]: functions = ['count', 'mean', 'max'] result = grouped['tip_pct', 'total_bill'].agg(functions) result # In[ ]: result['tip_pct'] # In[ ]: ftuples = [('Durchschnitt', 'mean'), ('Abweichung', np.var)] grouped['tip_pct', 'total_bill'].agg(ftuples) # In[ ]: grouped.agg({'tip' : np.max, 'size' : 'sum'}) grouped.agg({'tip_pct' : ['min', 'max', 'mean', 'std'], 'size' : 'sum'}) # ### Returning Aggregated Data Without Row Indexes # In[ ]: tips.groupby(['day', 'smoker'], as_index=False).mean() # ## Apply: General split-apply-combine # In[ ]: def top(df, n=5, column='tip_pct'): return df.sort_values(by=column)[-n:] top(tips, n=6) # In[ ]: tips.groupby('smoker').apply(top) # In[ ]: tips.groupby(['smoker', 'day']).apply(top, n=1, column='total_bill') # In[ ]: result = tips.groupby('smoker')['tip_pct'].describe() result result.unstack('smoker') # f = lambda x: x.describe() # grouped.apply(f) # ### Suppressing the Group Keys # In[ ]: tips.groupby('smoker', group_keys=False).apply(top) # ### Quantile and Bucket Analysis # In[ ]: frame = pd.DataFrame({'data1': np.random.randn(1000), 'data2': np.random.randn(1000)}) quartiles = pd.cut(frame.data1, 4) quartiles[:10] # In[ ]: def get_stats(group): return {'min': group.min(), 'max': group.max(), 'count': group.count(), 'mean': group.mean()} grouped = frame.data2.groupby(quartiles) grouped.apply(get_stats).unstack() # In[ ]: # Return quantile numbers grouping = pd.qcut(frame.data1, 10, labels=False) grouped = frame.data2.groupby(grouping) grouped.apply(get_stats).unstack() # ### Example: Filling Missing Values with Group-Specific Values # In[ ]: s = pd.Series(np.random.randn(6)) s[::2] = np.nan s s.fillna(s.mean()) # In[ ]: states = ['Ohio', 'New York', 'Vermont', 'Florida', 'Oregon', 'Nevada', 'California', 'Idaho'] group_key = ['East'] * 4 + ['West'] * 4 data = pd.Series(np.random.randn(8), index=states) data # In[ ]: data[['Vermont', 'Nevada', 'Idaho']] = np.nan data data.groupby(group_key).mean() # In[ ]: fill_mean = lambda g: g.fillna(g.mean()) data.groupby(group_key).apply(fill_mean) # In[ ]: fill_values = {'East': 0.5, 'West': -1} fill_func = lambda g: g.fillna(fill_values[g.name]) data.groupby(group_key).apply(fill_func) # ### Example: Random Sampling and Permutation # In[ ]: # Hearts, Spades, Clubs, Diamonds suits = ['H', 'S', 'C', 'D'] card_val = (list(range(1, 11)) + [10] * 3) * 4 base_names = ['A'] + list(range(2, 11)) + ['J', 'K', 'Q'] cards = [] for suit in ['H', 'S', 'C', 'D']: cards.extend(str(num) + suit for num in base_names) deck = pd.Series(card_val, index=cards) # In[ ]: deck[:13] # In[ ]: def draw(deck, n=5): return deck.sample(n) draw(deck) # In[ ]: get_suit = lambda card: card[-1] # last letter is suit deck.groupby(get_suit).apply(draw, n=2) # In[ ]: deck.groupby(get_suit, group_keys=False).apply(draw, n=2) # ### Example: Group Weighted Average and Correlation # In[ ]: df = pd.DataFrame({'category': ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'], 'data': np.random.randn(8), 'weights': np.random.rand(8)}) df # In[ ]: grouped = df.groupby('category') get_wavg = lambda g: np.average(g['data'], weights=g['weights']) grouped.apply(get_wavg) # In[ ]: close_px = pd.read_csv('examples/stock_px_2.csv', parse_dates=True, index_col=0) close_px.info() close_px[-4:] # In[ ]: spx_corr = lambda x: x.corrwith(x['SPX']) # In[ ]: rets = close_px.pct_change().dropna() # In[ ]: get_year = lambda x: x.year by_year = rets.groupby(get_year) by_year.apply(spx_corr) # In[ ]: by_year.apply(lambda g: g['AAPL'].corr(g['MSFT'])) # ### Example: Group-Wise Linear Regression # In[ ]: import statsmodels.api as sm def regress(data, yvar, xvars): Y = data[yvar] X = data[xvars] X['intercept'] = 1. result = sm.OLS(Y, X).fit() return result.params # In[ ]: by_year.apply(regress, 'AAPL', ['SPX']) # ## Pivot Tables and Cross-Tabulation # In[ ]: tips.pivot_table(index=['day', 'smoker']) # In[ ]: tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'], columns='smoker') # In[ ]: tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'], columns='smoker', margins=True) # In[ ]: tips.pivot_table('tip_pct', index=['time', 'smoker'], columns='day', aggfunc=len, margins=True) # In[ ]: tips.pivot_table('tip_pct', index=['time', 'size', 'smoker'], columns='day', aggfunc='mean', fill_value=0) # ### Cross-Tabulations: Crosstab # In[ ]: from io import StringIO data = """\ Sample Nationality Handedness 1 USA Right-handed 2 Japan Left-handed 3 USA Right-handed 4 Japan Right-handed 5 Japan Left-handed 6 Japan Right-handed 7 USA Right-handed 8 USA Left-handed 9 Japan Right-handed 10 USA Right-handed""" data = pd.read_table(StringIO(data), sep='\s+') # In[ ]: data # In[ ]: pd.crosstab(data.Nationality, data.Handedness, margins=True) # In[ ]: pd.crosstab([tips.time, tips.day], tips.smoker, margins=True) # In[ ]: pd.options.display.max_rows = PREVIOUS_MAX_ROWS # ## Conclusion