[英]pandas.DataFrame: How to align / group and sort data by index?
我是熊貓的新手,仍然沒有很好地了解它的力量和如何使用它。 所以這個問題希望很簡單:)
我有一個帶有日期索引和幾列(股票及其開盤價和收盤價)的 DataFrame。 以下是兩只股票A
和B
一些示例數據:
import pandas as pd
_ = pd.to_datetime
A_dt = [_('2018-01-04'), _('2018-01-01'), _('2018-01-05')]
B_dt = [_('2018-01-01'), _('2018-01-05'), _('2018-01-03'), _('2018-01-02')]
A_data = [(12, 11), (10, 9), (8, 9)]
B_data = [(2, 2), (3, 4), (4, 4), (5, 3)]
如您所見,數據不完整,每個系列的缺失日期不同。 我想將這些數據放在一個帶有排序的行索引dt
和 4 列(每只 2 只股票 x 2 個時間序列)的單個數據框中。
當我這樣做時,一切正常(除了我想更改列級別但不知道該怎么做):
# MultiIndex on axis 0, then unstacking
i0_a = pd.MultiIndex.from_tuples([("A", x) for x in A_dt], names=['symbol', 'dt'])
i0_b = pd.MultiIndex.from_tuples([("B", x) for x in B_dt], names=['symbol', 'dt'])
df0_a = pd.DataFrame(A_data, index=i0_a, columns=["Open", "Close"])
df0_b = pd.DataFrame(B_data, index=i0_b, columns=["Open", "Close"])
df = pd.concat([df0_a, df0_b])
df = df.unstack('symbol') # this automatically sorts by dt.
print df
# Open Close
#symbol A B A B
#dt
#2018-01-01 10.0 2.0 9.0 2.0
#2018-01-02 NaN 5.0 NaN 3.0
#2018-01-03 NaN 4.0 NaN 4.0
#2018-01-04 12.0 NaN 11.0 NaN
#2018-01-05 8.0 3.0 9.0 4.0
但是,當我將 MultiIndex 放在列上時,情況就不一樣了
# MultiIndex on axis 1
i1_a = pd.MultiIndex.from_tuples([("A", "Open"), ("A", "Close")], names=['symbol', 'series'])
i1_b = pd.MultiIndex.from_tuples([("B", "Open"), ("B", "Close")], names=['symbol', 'series'])
df1_a = pd.DataFrame(A_data, index=A_dt, columns=i1_a)
df1_b = pd.DataFrame(B_data, index=B_dt, columns=i1_b)
df = pd.concat([df1_a, df1_b])
print df
#symbol A B
#series Close Open Close Open
#2018-01-04 11.0 12.0 NaN NaN
#2018-01-01 9.0 10.0 NaN NaN
#2018-01-05 9.0 8.0 NaN NaN
#2018-01-01 NaN NaN 2.0 2.0
#2018-01-05 NaN NaN 4.0 3.0
#2018-01-03 NaN NaN 4.0 4.0
#2018-01-02 NaN NaN 3.0 5.0
編輯:使用 jezraels 回答我計時了 3 種不同的連接/組合數據幀的方法。 我的第一種方法是最快的。 結果證明使用combine_first
比其他方法慢一個數量級。 在示例中,數據的大小仍然非常小:
import timeit
setup = """
import pandas as pd
import numpy as np
stocks = 20
steps = 20
features = 10
data = []
index_method1 = []
index_method2 = []
cols_method1 = []
cols_method2 = []
df = None
for s in range(stocks):
name = "stock{0}".format(s)
index = np.arange(steps)
data.append(np.random.rand(steps, features))
index_method1.append(pd.MultiIndex.from_tuples([(name, x) for x in index], names=['symbol', 'dt']))
index_method2.append(index)
cols_method1.append([chr(65 + x) for x in range(features)])
cols_method2.append(pd.MultiIndex.from_arrays([[name] * features, [chr(65 + x) for x in range(features)]], names=['symbol', 'series']))
"""
method1 = """
for s in range(stocks):
df_new = pd.DataFrame(data[s], index=index_method1[s], columns=cols_method1[s])
if s == 0:
df = df_new
else:
df = pd.concat([df, df_new])
df = df.unstack('symbol')
"""
method2 = """
for s in range(stocks):
df_new = pd.DataFrame(data[s], index=index_method2[s], columns=cols_method2[s])
if s == 0:
df = df_new
else:
df = df.combine_first(df_new)
"""
method3 = """
for s in range(stocks):
df_new = pd.DataFrame(data[s], index=index_method2[s], columns=cols_method2[s])
if s == 0:
df = df_new.stack()
else:
df = pd.concat([df, df_new.stack()], axis=1)
df = df.unstack().swaplevel(0,1, axis=1).sort_index(axis=1)
"""
print ("Multi-Index axis 0, then concat: {} s".format((timeit.timeit(method1, setup, number=1))))
print ("Multi-Index axis 1, combine_first: {} s".format((timeit.timeit(method2, setup, number=1))))
print ("Stack and then concat: {} s".format((timeit.timeit(method3, setup, number=1))))
Multi-Index axis 0, then concat: 0.134283173989 s
Multi-Index axis 1, combine_first: 5.02396191049 s
Stack and then concat: 0.272278263371 s
這是問題,因為兩個 DataFrames 在列中都有不同的MultiIndex
,所以沒有對齊。
解決方案是stack
Series
, concat
到 2 列DataFrame
,然后DataFrame
unstack
並為MultiIndex
添加swaplevel
和sort_index
正確順序:
df = (pd.concat([df1_a.stack(), df1_b.stack()], axis=1)
.unstack()
.swaplevel(0,1, axis=1)
.sort_index(axis=1))
print (df)
series Close Open
symbol A B A B
2018-01-01 9.0 2.0 10.0 2.0
2018-01-02 NaN 3.0 NaN 5.0
2018-01-03 NaN 4.0 NaN 4.0
2018-01-04 11.0 NaN 12.0 NaN
2018-01-05 9.0 4.0 8.0 3.0
但更好的是使用combine_first
:
df = df1_a.combine_first(df1_b)
print (df)
symbol A B
series Close Open Close Open
2018-01-01 9.0 10.0 2.0 2.0
2018-01-02 NaN NaN 3.0 5.0
2018-01-03 NaN NaN 4.0 4.0
2018-01-04 11.0 12.0 NaN NaN
2018-01-05 9.0 8.0 4.0 3.0
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