重新索引Bins索引DataFrame

Question

我想为应用groupby:后未出现在索引/列中的bin添加带零的行和列

import numpy as np
import pandas as pd
bins = np.arange(-0.1, 2, 0.1)
names = np.random.random_integers(0, 100, 1000)
a = np.random.random(1000)
b = np.random.random(1000)
matrix = pd.DataFrame([names, pd.cut(a, bins), pd.cut(b, bins)]).T
matrix.columns = ['names', 'a', 'b']
matrix = matrix.groupby(['a', 'b']).count()
matrix.reset_index(inplace=True)
matrix = matrix.pivot(index='a', columns='b', values='names').fillna(0)

Answer 1

将pandas.cut方法的输出分配给变量以访问categories属性：

bins = np.arange(-0.1, 2, 0.1)
names = np.random.random_integers(0, 100, 1000)
a = np.random.random(1000)
b = np.random.random(1000)

##############################
# Use pd.cut like this
a_bins = pd.cut(a, bins)
b_bins = pd.cut(b, bins)
##############################

matrix = pd.DataFrame([names, a_bins, b_bins]).T

matrix.columns = ['names', 'a', 'b']
matrix = matrix.groupby(['a', 'b']).count()
matrix.reset_index(inplace=True)
matrix = matrix.pivot(index='a', columns='b', values='names').fillna(0)
##################################################
# Reindex with this
matrix = matrix.reindex(index=a_bins.categories,
columns=b_bins.categories,
fill_value=0)
##################################################