放大设置:如何将一行数据帧添加到另一个数据帧

你需要 loc 对于放大设置,请选择 index 价值依据 [0] 对于标量和最后一个“转换” row_one iloc :

df.loc[row_one.index[0]] = row_one.iloc[0]
print (df)
            open  high  low  close  volume  open_interest
2017-01-01  10.0  11.0  9.0   10.0   100.0            NaN

但更好的是使用 concat ,尤其是在多个 df s:

df = pd.concat([row_one, row_two])

我从事件中获取新数据,因此每次只需添加一行数据帧。

每次迭代都需要完整、更新的数据帧吗?

如果没有,请执行以下操作:

new_row_data = {'open': 10.0,
 'high': 11.0,
 'low': 9.0,
 'close': 10.0,
 'volume': 100.0,
 'open_interest': np.nan}
new_row_index = pd.Timestamp('2017-01-01 00:00:00')

index = []
records = []
for _ in range(500):
    index.append(new_row_index)
    records.append(new_row_data)  # add new data here

# Create dataframe at the end
df = pd.DataFrame.from_records(records, index=index)

(上面的代码大约需要2.4毫秒)。

buffer_size = 100  # adjust to your needs
data_columns = ["open","high","low","close","volume","open_interest"]
all_columns = ['DateTime'] + data_columns  # Add column for datetimes
df_empty = pd.DataFrame(None, index=range(buffer_size),
                        columns=all_columns)
# Note: You might want to specify dtypes above rather than np.nans

df = df_empty.copy()
index = 0
for _ in range(500):
    df.loc[index, 'DateTime'] = new_row_index
    df.loc[index, columns] = new_row_data  # add new data here
    # Updated dataframe if you need it:
    #print(df.loc[:index])

    index += 1
    while index >= len(df):
        df = pd.concat([df, df_empty.reindex(range(index, index + buffer_size))])

# To remove the integer index use:
df = df.loc[:index-1].set_index('DateTime', drop=True)

(上面的代码大约需要540毫秒)。

concat 或 append