我使用Pandas和他的DataReader编写了这个简单的脚本来从yahoo finance检索多个股票结果：
import pandas as pd
from pandas.io.data import DataReader
from pandas import DataFrame
symbols_list = ['AAPL', 'TSLA', 'YHOO','GOOG', 'MSFT','GILD']
for ticker in symbols_list:
r = DataReader(ticker, "yahoo", '2015-01-20')
cell= r[['Open','High','Low','Adj Close','Volume']]
print cell
使用此代码,我获得价格股票,其中包含我在“cell = r [[….”中指定的其他列的日期,如下所示：
Open High Low Adj Close Volume
Date
2015-01-20 107.84 108.97 106.50 108.72 49899900
2015-01-21 108.95 111.06 108.27 109.55 48575900
2015-01-22 110.26 112.47 109.72 112.40 53796400
2015-01-23 112.30 113.75 111.53 112.98 46464800
2015-01-26 113.74 114.36 112.80 113.10 55375900
Open High Low Adj Close Volume
Date
2015-01-20 193.87 194.12 187.04 191.93 4489400
2015-01-21 189.55 198.68 189.51 196.57 4144000
2015-01-22 197.00 203.24 195.20 201.62 4094100
2015-01-23 200.29 203.50 198.33 201.29 3438600
2015-01-26 201.83 208.62 201.05 206.55 3224500
我的问题是：如何在列中包含我在symbol_list中指定的代码？
最后一件事：我怎样才能颠倒日期的顺序？我想让它显示最新的第一个(在我的例子中为2015-01-26).
下面我将向您展示我想要获取的内容的示例(作为第一列的股票代号和倒置的日期顺序)
TSLA 2015-01-26 201.83 208.62 201.05 206.55 3224500
TSLA 2015-01-23 200.29 203.50 198.33 201.29 3438600
TSLA 2015-01-22 197.00 203.24 195.20 201.62 4094100
TSLA,2015-01-21 189.55 198.68 189.51 196.57 4144000
TSLA 2015-01-20 193.87 194.12 187.04 191.93 4489400
AAPL 2015-01-26 113.74 114.36 112.80 113.10 55375900
AAPL 2015-01-23 112.30 113.75 111.53 112.98 46464800
AAPL 2015-01-22 110.26 112.47 109.72 112.40 53796400
AAPL 2015-01-21 108.95 111.06 108.27 109.55 48575900
AAPL 2015-01-20 107.84 108.97 106.50 108.72 49899900
我尝试了一些在论坛中创建的东西,但没有一个给了我任何结果.
谢谢大家的考虑,希望有人能帮我解决这两个问题.
解决方法:
我想我理解你想要的东西,我会将每个df附加到一个列表并使用concat来制作一个df,对于每个df,我们可以为每个符号添加一个新列：
In [54]:
from pandas.io.data import DataReader
symbols_list = ['AAPL', 'TSLA', 'YHOO','GOOG', 'MSFT','GILD']
symbols=[]
for ticker in symbols_list:
r = DataReader(ticker, "yahoo", '2015-01-20')
# add a symbol column
r['Symbol'] = ticker
symbols.append(r)
# concatenate all the dfs
df = pd.concat(symbols)
df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 30 entries, 2015-01-20 00:00:00 to 2015-01-26 00:00:00
Data columns (total 7 columns):
Open 30 non-null float64
High 30 non-null float64
Low 30 non-null float64
Close 30 non-null float64
Volume 30 non-null int64
Adj Close 30 non-null float64
Symbol 30 non-null object
dtypes: float64(5), int64(1), object(1)
memory usage: 1.9+ KB
In [55]:
df
Out[55]:
Open High Low Close Volume Adj Close Symbol
Date
2015-01-20 107.84 108.97 106.50 108.72 49899900 108.72 AAPL
2015-01-21 108.95 111.06 108.27 109.55 48575900 109.55 AAPL
2015-01-22 110.26 112.47 109.72 112.40 53796400 112.40 AAPL
2015-01-23 112.30 113.75 111.53 112.98 46464800 112.98 AAPL
2015-01-26 113.74 114.36 112.80 113.10 55375900 113.10 AAPL
2015-01-20 193.87 194.12 187.04 191.93 4503200 191.93 TSLA
2015-01-21 189.55 198.68 189.51 196.57 4153000 196.57 TSLA
2015-01-22 197.00 203.24 195.20 201.62 4094100 201.62 TSLA
2015-01-23 200.29 203.50 198.33 201.29 3438600 201.29 TSLA
2015-01-26 201.83 208.62 201.05 206.55 3224500 206.55 TSLA
2015-01-20 46.79 47.89 46.77 47.63 15845900 47.63 YHOO
2015-01-21 47.60 48.38 47.32 48.18 16305100 48.18 YHOO
2015-01-22 48.43 49.08 48.01 48.89 12647400 48.89 YHOO
2015-01-23 48.74 49.23 48.63 48.95 14206100 48.95 YHOO
2015-01-26 49.57 49.79 49.07 49.44 18928700 49.44 YHOO
2015-01-20 511.00 512.50 506.02 506.90 2221800 506.90 GOOG
2015-01-21 507.25 519.28 506.20 518.04 2262500 518.04 GOOG
2015-01-22 521.48 536.33 519.70 534.39 2653600 534.39 GOOG
2015-01-23 535.59 542.17 533.00 539.95 2266800 539.95 GOOG
2015-01-26 538.53 539.00 529.67 535.21 1532400 535.21 GOOG
2015-01-20 46.30 46.65 45.57 46.39 36161900 46.39 MSFT
2015-01-21 45.94 46.14 45.48 45.92 39081100 45.92 MSFT
2015-01-22 46.38 47.14 46.08 47.13 35898000 47.13 MSFT
2015-01-23 47.36 47.39 46.80 47.18 26211600 47.18 MSFT
2015-01-26 47.00 47.13 46.24 47.01 39577800 47.01 MSFT
2015-01-20 101.14 104.05 100.15 103.78 18838100 103.78 GILD
2015-01-21 103.04 105.07 102.57 103.58 12312500 103.58 GILD
2015-01-22 104.14 105.18 101.56 105.14 12963100 105.14 GILD
2015-01-23 104.40 106.11 103.80 105.54 9566700 105.54 GILD
2015-01-26 104.87 107.77 104.87 107.11 10254600 107.11 GILD
In [56]:
df.sort(ascending=False)
Out[56]:
Open High Low Close Volume Adj Close Symbol
Date
2015-01-26 104.87 107.77 104.87 107.11 10254600 107.11 GILD
2015-01-26 201.83 208.62 201.05 206.55 3224500 206.55 TSLA
2015-01-26 538.53 539.00 529.67 535.21 1532400 535.21 GOOG
2015-01-26 49.57 49.79 49.07 49.44 18928700 49.44 YHOO
2015-01-26 47.00 47.13 46.24 47.01 39577800 47.01 MSFT
2015-01-26 113.74 114.36 112.80 113.10 55375900 113.10 AAPL
2015-01-23 104.40 106.11 103.80 105.54 9566700 105.54 GILD
2015-01-23 47.36 47.39 46.80 47.18 26211600 47.18 MSFT
2015-01-23 200.29 203.50 198.33 201.29 3438600 201.29 TSLA
2015-01-23 535.59 542.17 533.00 539.95 2266800 539.95 GOOG
2015-01-23 48.74 49.23 48.63 48.95 14206100 48.95 YHOO
2015-01-23 112.30 113.75 111.53 112.98 46464800 112.98 AAPL
2015-01-22 521.48 536.33 519.70 534.39 2653600 534.39 GOOG
2015-01-22 46.38 47.14 46.08 47.13 35898000 47.13 MSFT
2015-01-22 110.26 112.47 109.72 112.40 53796400 112.40 AAPL
2015-01-22 48.43 49.08 48.01 48.89 12647400 48.89 YHOO
2015-01-22 104.14 105.18 101.56 105.14 12963100 105.14 GILD
2015-01-22 197.00 203.24 195.20 201.62 4094100 201.62 TSLA
2015-01-21 507.25 519.28 506.20 518.04 2262500 518.04 GOOG
2015-01-21 189.55 198.68 189.51 196.57 4153000 196.57 TSLA
2015-01-21 47.60 48.38 47.32 48.18 16305100 48.18 YHOO
2015-01-21 103.04 105.07 102.57 103.58 12312500 103.58 GILD
2015-01-21 45.94 46.14 45.48 45.92 39081100 45.92 MSFT
2015-01-21 108.95 111.06 108.27 109.55 48575900 109.55 AAPL
2015-01-20 101.14 104.05 100.15 103.78 18838100 103.78 GILD
2015-01-20 107.84 108.97 106.50 108.72 49899900 108.72 AAPL
2015-01-20 46.30 46.65 45.57 46.39 36161900 46.39 MSFT
2015-01-20 46.79 47.89 46.77 47.63 15845900 47.63 YHOO
2015-01-20 193.87 194.12 187.04 191.93 4503200 191.93 TSLA
2015-01-20 511.00 512.50 506.02 506.90 2221800 506.90 GOOG
更新
如果要保留股票代码分组,然后按日期排序,则需要以下内容.在这里,我必须重置索引,以便’Date’再次成为常规列,然后我传递一个列表,它将按顺序处理,最后我必须再次设置索引：
In [44]:
df.reset_index().sort(['Symbol', 'Date'], ascending=False).set_index('Date')
Out[44]:
Open High Low Close Volume Adj Close Symbol
Date
2015-01-27 49.14 49.28 47.66 47.99 44813600 47.99 YHOO
2015-01-26 49.57 49.79 49.07 49.44 18928700 49.44 YHOO
2015-01-23 48.74 49.23 48.63 48.95 14206100 48.95 YHOO
2015-01-22 48.43 49.08 48.01 48.89 12647400 48.89 YHOO
2015-01-21 47.60 48.38 47.32 48.18 16305100 48.18 YHOO
2015-01-20 46.79 47.89 46.77 47.63 15845900 47.63 YHOO
2015-01-27 204.42 208.03 203.30 205.98 2777700 205.98 TSLA
2015-01-26 201.83 208.62 201.05 206.55 3224500 206.55 TSLA
2015-01-23 200.29 203.50 198.33 201.29 3438600 201.29 TSLA
2015-01-22 197.00 203.24 195.20 201.62 4094100 201.62 TSLA
2015-01-21 189.55 198.68 189.51 196.57 4153000 196.57 TSLA
2015-01-20 193.87 194.12 187.04 191.93 4503200 191.93 TSLA
2015-01-27 42.95 43.20 42.11 42.66 169112700 42.66 MSFT
2015-01-26 47.00 47.13 46.24 47.01 42525500 47.01 MSFT
2015-01-23 47.36 47.39 46.80 47.18 26211600 47.18 MSFT
2015-01-22 46.38 47.14 46.08 47.13 35898000 47.13 MSFT
2015-01-21 45.94 46.14 45.48 45.92 39081100 45.92 MSFT
2015-01-20 46.30 46.65 45.57 46.39 36161900 46.39 MSFT
2015-01-27 529.97 530.70 518.19 518.63 1897300 518.63 GOOG
2015-01-26 538.53 539.00 529.67 535.21 1532400 535.21 GOOG
2015-01-23 535.59 542.17 533.00 539.95 2266800 539.95 GOOG
2015-01-22 521.48 536.33 519.70 534.39 2653600 534.39 GOOG
2015-01-21 507.25 519.28 506.20 518.04 2262500 518.04 GOOG
2015-01-20 511.00 512.50 506.02 506.90 2221800 506.90 GOOG
2015-01-27 105.94 107.02 104.86 105.03 10334500 105.03 GILD
2015-01-26 104.87 107.77 104.87 107.11 10254600 107.11 GILD
2015-01-23 104.40 106.11 103.80 105.54 9566700 105.54 GILD
2015-01-22 104.14 105.18 101.56 105.14 12963100 105.14 GILD
2015-01-21 103.04 105.07 102.57 103.58 12312500 103.58 GILD
2015-01-20 101.14 104.05 100.15 103.78 18838100 103.78 GILD
2015-01-27 112.42 112.48 109.03 109.14 91929200 109.14 AAPL
2015-01-26 113.74 114.36 112.80 113.10 55615000 113.10 AAPL
2015-01-23 112.30 113.75 111.53 112.98 46464800 112.98 AAPL
2015-01-22 110.26 112.47 109.72 112.40 53796400 112.40 AAPL
2015-01-21 108.95 111.06 108.27 109.55 48575900 109.55 AAPL
2015-01-20 107.84 108.97 106.50 108.72 49899900 108.72 AAPL
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