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)
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
grouped = df['data1'].groupby(df['key1'])
grouped
grouped.mean()
means = df['data1'].groupby([df['key1'], df['key2']]).mean()
means
means.unstack()
states = np.array(['Ohio', 'California', 'California', 'Ohio', 'Ohio'])
years = np.array([2005, 2005, 2006, 2005, 2006])
df['data1'].groupby([states, years]).mean()
df.groupby('key1').mean()
df.groupby(['key1', 'key2']).mean()
df.groupby(['key1', 'key2']).size()
for name, group in df.groupby('key1'):
print(name)
print(group)
for (k1, k2), group in df.groupby(['key1', 'key2']):
print((k1, k2))
print(group)
pieces = dict(list(df.groupby('key1')))
pieces['b']
df.dtypes
grouped = df.groupby(df.dtypes, axis=1)
for dtype, group in grouped:
print(dtype)
print(group)
df.groupby('key1')['data1'] df.groupby('key1')[['data2']]
df['data1'].groupby(df['key1']) df[['data2']].groupby(df['key1'])
df.groupby(['key1', 'key2'])[['data2']].mean()
s_grouped = df.groupby(['key1', 'key2'])['data2']
s_grouped
s_grouped.mean()
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
mapping = {'a': 'red', 'b': 'red', 'c': 'blue',
'd': 'blue', 'e': 'red', 'f' : 'orange'}
by_column = people.groupby(mapping, axis=1)
by_column.sum()
map_series = pd.Series(mapping)
map_series
people.groupby(map_series, axis=1).count()
people.groupby(len).sum()
key_list = ['one', 'one', 'one', 'two', 'two']
people.groupby([len, key_list]).min()
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
hier_df.groupby(level='cty', axis=1).count()
df
grouped = df.groupby('key1')
grouped['data1'].quantile(0.9)
def peak_to_peak(arr):
return arr.max() - arr.min()
grouped.agg(peak_to_peak)
grouped.describe()
tips = pd.read_csv('examples/tips.csv')
# Add tip percentage of total bill
tips['tip_pct'] = tips['tip'] / tips['total_bill']
tips[:6]
grouped = tips.groupby(['day', 'smoker'])
grouped_pct = grouped['tip_pct']
grouped_pct.agg('mean')
grouped_pct.agg(['mean', 'std', peak_to_peak])
grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])
functions = ['count', 'mean', 'max']
result = grouped['tip_pct', 'total_bill'].agg(functions)
result
result['tip_pct']
ftuples = [('Durchschnitt', 'mean'), ('Abweichung', np.var)]
grouped['tip_pct', 'total_bill'].agg(ftuples)
grouped.agg({'tip' : np.max, 'size' : 'sum'})
grouped.agg({'tip_pct' : ['min', 'max', 'mean', 'std'],
'size' : 'sum'})
tips.groupby(['day', 'smoker'], as_index=False).mean()
def top(df, n=5, column='tip_pct'):
return df.sort_values(by=column)[-n:]
top(tips, n=6)
tips.groupby('smoker').apply(top)
tips.groupby(['smoker', 'day']).apply(top, n=1, column='total_bill')
result = tips.groupby('smoker')['tip_pct'].describe()
result
result.unstack('smoker')
f = lambda x: x.describe() grouped.apply(f)
tips.groupby('smoker', group_keys=False).apply(top)
frame = pd.DataFrame({'data1': np.random.randn(1000),
'data2': np.random.randn(1000)})
quartiles = pd.cut(frame.data1, 4)
quartiles[:10]
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()
# Return quantile numbers
grouping = pd.qcut(frame.data1, 10, labels=False)
grouped = frame.data2.groupby(grouping)
grouped.apply(get_stats).unstack()
s = pd.Series(np.random.randn(6))
s[::2] = np.nan
s
s.fillna(s.mean())
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
data[['Vermont', 'Nevada', 'Idaho']] = np.nan
data
data.groupby(group_key).mean()
fill_mean = lambda g: g.fillna(g.mean())
data.groupby(group_key).apply(fill_mean)
fill_values = {'East': 0.5, 'West': -1}
fill_func = lambda g: g.fillna(fill_values[g.name])
data.groupby(group_key).apply(fill_func)
# 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)
deck[:13]
def draw(deck, n=5):
return deck.sample(n)
draw(deck)
get_suit = lambda card: card[-1] # last letter is suit
deck.groupby(get_suit).apply(draw, n=2)
deck.groupby(get_suit, group_keys=False).apply(draw, n=2)
df = pd.DataFrame({'category': ['a', 'a', 'a', 'a',
'b', 'b', 'b', 'b'],
'data': np.random.randn(8),
'weights': np.random.rand(8)})
df
grouped = df.groupby('category')
get_wavg = lambda g: np.average(g['data'], weights=g['weights'])
grouped.apply(get_wavg)
close_px = pd.read_csv('examples/stock_px_2.csv', parse_dates=True,
index_col=0)
close_px.info()
close_px[-4:]
spx_corr = lambda x: x.corrwith(x['SPX'])
rets = close_px.pct_change().dropna()
get_year = lambda x: x.year
by_year = rets.groupby(get_year)
by_year.apply(spx_corr)
by_year.apply(lambda g: g['AAPL'].corr(g['MSFT']))
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
by_year.apply(regress, 'AAPL', ['SPX'])
tips.pivot_table(index=['day', 'smoker'])
tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'],
columns='smoker')
tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'],
columns='smoker', margins=True)
tips.pivot_table('tip_pct', index=['time', 'smoker'], columns='day',
aggfunc=len, margins=True)
tips.pivot_table('tip_pct', index=['time', 'size', 'smoker'],
columns='day', aggfunc='mean', fill_value=0)
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+')
data
pd.crosstab(data.Nationality, data.Handedness, margins=True)
pd.crosstab([tips.time, tips.day], tips.smoker, margins=True)
pd.options.display.max_rows = PREVIOUS_MAX_ROWS