Question

我有一个像这样的数据集：

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

此时间序列表示通用股票市场的价值。

我想计算“关闭”列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；每一行代表一天）。

我使用for循环完成了此操作（在大数据问题中使用熊猫很糟糕），并且在不同的DataFrame中创建了正确的结果：

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)

我该如何利用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

使用diff

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

Answer 2

我建议您首先将Date列用作DateTime索引，以供使用

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，并执行任何所需的操作即可，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进行操作：

for Dt in df_stock.index:

Answer 3

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

或者如果您想按相反顺序计算变化：

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

熊猫：计算两行之间的百分比，并将该值添加为一列

3 个答案: