1. pandas读取csv

import pandas as pd

df = pd.read_csv(<filename>)
print df.head()    # 头部5行
print df.tail()    # 尾部5行
print df.tail(n)    # 尾部n行
print df[10:21]   # 切片, 10到20行
print df['Close'].max()    # 统计出最大收盘价 

2. pandas绘制图表

"""Plot High prices for IBM"""

import pandas as pd
import matplotlib.pyplot as plt

def test_run():
    df = pd.read_csv("data/IBM.csv")
    df['High'].plot()
    # df[['Close', 'Adj Close']].plot() #绘制两列
    plt.xlabel('Time')
    plt.ylabel('High')
    plt.title('High prices for IBM')
    plt.show()  # must be called to show plots


if __name__ == "__main__":
    test_run()

csv中日期是从上向下递增的, 所以读进来的日期顺序是反过来的, 曲线也是反过来的, 呈下降趋势, 需进行反转处理.

3. 构建及合并DataFrame, df.join()

• 创建日期范围列

start_date = '2010-01-22'
end_date = '2010-01-26'
dates = pd.date_range(start_date, end_date)
df1 = pd.DataFrame(index = dates)  # 不指定index将使用0, 1, 2

date_range()返回datetime索引对象的列表[2010-01-22, ..., 2010-01-26]
In: dates[0]
Out: 2010-01-22 00:00:00 # 股票信息中时间戳可忽略

合并DataFrame:

dfSPY = pd.read_csv("data/SPY.csv", index_col = "Date", 
                    parse_dates = True, usecols = ['Date', 'Adj Close'],
                    na_values = ['nan'])
df1 = df1.join(dfSPY)

默认向左合并, dfSPY和df1索引中有交集的部分会被合并, 否则填充NaN, 所以会保留df1的全部数据.
SPY.csv默认是以整数进行索引的, 所以需指定索引列index_col, 并指定索引格式是datatime.对于NaN需告诉pandas不能当成一个数处理, 所以na_values指定NaN为字符串格式.
之后使用dropna()直接将df1中为NaN的行去掉:

df1 = df1.dropna()

二者其实可以直接用一句话完成, 即how参数为inner内联, 只取交集, 但是时间顺序为倒序

df1 = df1.join(dfSPY, how = 'inner')

附df.join()中how参数使用用法:
how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘left’
How to handle the operation of the two objects.
left: use calling frame’s(调用表) index (or column if on is specified)
right: use other’s index.(被调用表)
outer: form union of calling frame’s index (or column if on is specified) with other’s index, and sort it. lexicographically字典序.
inner: form intersection of calling frame’s index (or column if on is specified) with other’s index, preserving the order of the calling’s one.

在批量读入csv时, 为防止'Adj Close'列名冲突, 需要重新命名为csv文件的名字, 如下图所示:

df_temp = df_temp.rename(columns={'Adj Close': symbol})
df = df.join(df_temp)

DataFrame重命名

当使用SPY作为参考时，需要将不交易的日期删掉:

# TODO:对SPY去除空白列时需配合how参数为left, 若为inner则日期顺序会倒过来, 使用subset则为正序
df = df.join(df_temp, how='left')  
if symbol == 'SPY':
  df = df.dropna(subset=['SPY'])

纽约证交所一年交易日252天, SPY除了周末和节假日都开盘, 所以经常用SPY作为日期参考

4. DataFrame切片

• 行切片

df.ix['2010-01-01' : '2010-01-31']

• 列切片

df['GOOG']  # 单列
df[['IBM', 'GLD']]  # 多列要传入列表

• 行列同时切片

df.ix['2010-01-01' : '2010-01-31', ['IBM', 'GLD']]

5.图表相关

• 图表归一化:

df1 = df1 / df1[0] # 除以第一行做归一化, 所有曲线都从1美元开始

归一化的目的是凸显股票波动
注意：根据 pandas 语法，该操作应该读成：
df = df / df.ix[0]
或者，更明确地读成：
df = df / df.ix[0, :]
这种方法是基于C层面的, 比二层循环遍历整个图表的python层面更快

• 将df数据传入使用matplotlib.pyplot绘制图表:

import matplotlib.pyplot as plt
def plot_data(df, title="Stock prices"):
    """Plot stock prices with a custom title and meaningful axis labels."""
    ax = df.plot(title=title, fontsize=12)
    ax.set_xlabel("Date")
    ax.set_ylabel("Price")
    plt.show()

注意: set_xlabel()方法是调用df.plot后返回对象的方法, 可以理解为handler

全部代码如下:

"""Slice and plot"""

import os
import pandas as pd
import matplotlib.pyplot as plt

def plot_selected(df, columns, start_index, end_index):
    """Plot the desired columns over index values in the given range."""
    plot_data(df.ix[start_index : end_index, columns], title="Selected data")

def symbol_to_path(symbol, base_dir="data"):
    """Return CSV file path given ticker symbol."""
    return os.path.join(base_dir, "{}.csv".format(str(symbol)))

def get_data(symbols, dates):
    """Read stock data (adjusted close) for given symbols from CSV files."""
    df = pd.DataFrame(index=dates)
    if 'SPY' not in symbols:  # add SPY for reference, if absent
        symbols.insert(0, 'SPY')

    for symbol in symbols:
        df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',
                parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])
        df_temp = df_temp.rename(columns={'Adj Close': symbol})
        df = df.join(df_temp)
        if symbol == 'SPY':  # drop dates SPY did not trade
            df = df.dropna(subset=["SPY"])

    return df

def plot_data(df, title="Stock prices"):
    """Plot stock prices with a custom title and meaningful axis labels."""
    ax = df.plot(title=title, fontsize=12)
    ax.set_xlabel("Date")
    ax.set_ylabel("Price")
    plt.show()

def normalize_data(df):
    """Normalize stock prices using the first row of the dataframe"""
    return df / df.ix[0, :]

def test_run():
    # Define a date range
    dates = pd.date_range('2010-01-01', '2010-12-31')
    # Choose stock symbols to read
    symbols = ['GOOG', 'IBM', 'GLD']  # SPY will be added in get_data()
    # Get stock data
    df = get_data(symbols, dates)
    # Normalize stock prices
    df = normalize_data(df)  
    # Slice and plot
    plot_selected(df, ['SPY', 'IBM'], '2010-03-01', '2010-04-01')

if __name__ == "__main__":
    test_run()