我有一个结构如下的数据集：
"Date","Time","Open","High","Low","Close","Volume"
这个时间序列代表了一个普通股票市场的价值。
我想计算"收盘"列两行之间的百分比差异(事实上，我想知道股票的价值增加或减少了多少；每行代表一天(。
我用for循环完成了这项工作(在大数据问题中使用panda非常糟糕(，我创建了正确的结果，但使用了不同的DataFrame:
rows_number = df_stock.shape[0]
# The first row will be 1, because is calculated in percentage. If haven't any yesterday the value must be 1
percentage_df = percentage_df.append({'Date': df_stock.iloc[0]['Date'], 'Percentage': 1}, ignore_index=True)
# Foreach days, calculate the market trend in percentage
for index in range(1, rows_number):
# n_yesterday : 100 = (n_today - n_yesterday) : x
n_today = df_stock.iloc[index]['Close']
n_yesterday = self.df_stock.iloc[index-1]['Close']
difference = n_today - n_yesterday
percentage = (100 * difference ) / n_yesterday
percentage_df = percentage_df .append({'Date': df_stock.iloc[index]['Date'], 'Percentage': percentage}, ignore_index=True)
我如何利用dataFrameapi重构它，从而删除for循环并在适当的位置创建一个新列？
df['Change'] = df['Close'].pct_change()
或者如果你想以相反的顺序计算变化：
df['Change'] = df['Close'].pct_change(-1)
我建议首先将Date列作为DateTime索引。为此，您可以使用
df_stock = df_stock.set_index(['Date'])
df_stock.index = pd.to_datetime(df_stock.index, dayfirst=True)
然后，只需使用日期时间索引访问具有特定列的任何行，并执行任何类型的操作，例如计算列"关闭"的两行之间的百分比差异
df_stock['percentage'] = ((df_stock['15-07-2019']['Close'] - df_stock['14-07-2019']['Close'])/df_stock['14-07-2019']['Close']) * 100
您也可以使用for循环为每个日期或行执行操作：
for Dt in df_stock.index:
使用diff
(-df['Close'].diff())/df['Close'].shift()