创建一个空的 Pandas DataFrame，然后填充它? [英] Creating an empty Pandas DataFrame, then filling it?

问题描述

我从这里的 Pandas DataFrame 文档开始:http://pandas.pydata.org/pandas-docs/stable/dsintro.html

I'm starting from the pandas DataFrame docs here: http://pandas.pydata.org/pandas-docs/stable/dsintro.html

我想用时间序列类型的计算中的值迭代地填充 DataFrame.所以基本上，我想用 A 列、B 列和时间戳行初始化 DataFrame，全部为 0 或全部为 NaN.

I'd like to iteratively fill the DataFrame with values in a time series kind of calculation. So basically, I'd like to initialize the DataFrame with columns A, B and timestamp rows, all 0 or all NaN.

然后我会添加初始值并检查这些数据，计算前一行的新行，比如 row[A][t] = row[A][t-1]+1 左右.

I'd then add initial values and go over this data calculating the new row from the row before, say row[A][t] = row[A][t-1]+1 or so.

我目前正在使用下面的代码，但我觉得它有点难看，必须有一种方法可以直接使用 DataFrame 来做到这一点，或者一般来说只是一种更好的方法.注意:我使用的是 Python 2.7.

I'm currently using the code as below, but I feel it's kind of ugly and there must be a way to do this with a DataFrame directly, or just a better way in general. Note: I'm using Python 2.7.

import datetime as dt
import pandas as pd
import scipy as s

if __name__ == '__main__':
    base = dt.datetime.today().date()
    dates = [ base - dt.timedelta(days=x) for x in range(0,10) ]
    dates.sort()

    valdict = {}
    symbols = ['A','B', 'C']
    for symb in symbols:
        valdict[symb] = pd.Series( s.zeros( len(dates)), dates )

    for thedate in dates:
        if thedate > dates[0]:
            for symb in valdict:
                valdict[symb][thedate] = 1+valdict[symb][thedate - dt.timedelta(days=1)]

    print valdict

推荐答案

这里有几个建议:

使用 date_range 用于索引:

Use date_range for the index:

import datetime
import pandas as pd
import numpy as np

todays_date = datetime.datetime.now().date()
index = pd.date_range(todays_date-datetime.timedelta(10), periods=10, freq='D')

columns = ['A','B', 'C']

注意:我们可以简单地通过编写:

df_ = pd.DataFrame(index=index, columns=columns)
df_ = df_.fillna(0) # with 0s rather than NaNs

要对数据进行这些类型的计算，请使用 numpy 数组:

To do these type of calculations for the data, use a numpy array:

data = np.array([np.arange(10)]*3).T

因此我们可以创建 DataFrame:

Hence we can create the DataFrame:

In [10]: df = pd.DataFrame(data, index=index, columns=columns)

In [11]: df
Out[11]: 
            A  B  C
2012-11-29  0  0  0
2012-11-30  1  1  1
2012-12-01  2  2  2
2012-12-02  3  3  3
2012-12-03  4  4  4
2012-12-04  5  5  5
2012-12-05  6  6  6
2012-12-06  7  7  7
2012-12-07  8  8  8
2012-12-08  9  9  9

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