Pandas：计算两行之间的百分比并将值添加为列

Question

我有一个这样结构的数据集：

"Date","Time","Open","High","Low","Close","Volume"

此时间序列代表一般股票市场的价值。

我想计算“收盘”列的两行之间的百分比差异（实际上，我想知道股票的价值增加或减少了多少；每行代表一天）。

我已经用 for 循环完成了这个（在大数据问题中使用 Pandas 很糟糕），我创建了正确的结果，但在不同的 DataFrame 中：

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)

我怎样才能利用 dataFrame api 重构它，从而删除 for 循环并在适当的位置创建一个新列？

Answer 1

我建议首先将 Date 列作为 DateTime 索引，您可以使用

df_stock = df_stock.set_index(['Date'])
df_stock.index = pd.to_datetime(df_stock.index, dayfirst=True)

然后通过使用日期时间索引简单地访问具有特定列的任何行，并根据需要执行任何类型的操作，例如计算“关闭”列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

您还可以使用 for 循环对每个日期或行执行操作：

for Dt in df_stock.index:

Answer 2

df['Change'] = df['Close'].pct_change()

或者如果你想以相反的顺序改变计算：

df['Change'] = df['Close'].pct_change(-1)

Answer 3

使用diff

(-df['Close'].diff())/df['Close'].shift()