使用列名创建一个空的数据框或列表，然后按列名添加数据

Question

I am trying to learn python 2.7 by converting code I wrote in VB to python. I have column names and I am trying to create a empty dataframe or list then add rows by iterating (see below). I do not know the total number of rows I will need to add in advance. I can create a dataframe with the column names but can't figure out how to add the data. I have looked at several questions like mine but the row/columns of data are unknown in advance.

snippet of code:

cnames=['Security','Time','Vol_21D','Vol2_21D','MaxAPV_21D','MinAPV_21D' ]
df_Calcs = pd.DataFrame(index=range(10), columns=cnames)

this creates the empty df (df_Calcs)...then the code below is where I get the data to fill the rows...I use n as a counter for the new row # to insert (there are 20 other columns that I add to the row), but the below should explain what I am trying to do.

i = 0
n = 0
while True:
        df_Calcs.Security[n] = i + 1
        df_Calcs.Time[n] = '09:30:00'
        df_Calcs.Vol_21D[n] = i + 2
        df_Calcs.Vol2_21D[n] = i + 3
        df_Calcs.MaxAPV_21D[n] = i + 4
        df_Calcs.MinAPV_21D[n] = i + 5
        i = i +1
        n = n +1
        if i > 4:
           break

print df_Calcs If I should use a list or array instead please let me know, I am trying to do this in the fastest most efficient way. This data will then be sent to a MySQL db table.

Result...

  Security      Time Vol_21D Vol2_21D MaxAPV_21D MinAPV_21D
0        1  09:30:00       2        3          4          5
1        2  09:30:00       3        4          5          6
2        3  09:30:00       4        5          6          7
3        4  09:30:00       5        6          7          8
4        5  09:30:00       6        7          8          9
5      NaN       NaN     NaN      NaN        NaN        NaN
6      NaN       NaN     NaN      NaN        NaN        NaN
7      NaN       NaN     NaN      NaN        NaN        NaN
8      NaN       NaN     NaN      NaN        NaN        NaN
9      NaN       NaN     NaN      NaN        NaN        NaN

Answer 1

You have many ways to do that.

Create empty dataframe:

cnames=['Security', 'Time', 'Vol_21D', 'Vol2_21D', 'MaxAPV_21D', 'MinAPV_21D']
df = pd.DataFrame(columns=cnames)

Output:

Empty DataFrame
Columns: [Security, Time, Vol_21D, Vol2_21D, MaxAPV_21D, MinAPV_21D]
Index: []

Then, in loop you can create a pd.series and append to your dataframe, example:

df.append(pd.Series([1, 2, 3, 4, 5, 6], cnames), ignore_index=True)

Or you can append a dict:

df.append({'Security': 1,
           'Time': 2,
           'Vol_21D': 3,
           'Vol2_21D': 4,
           'MaxAPV_21D': 5,
           'MinAPV_21D': 6
          }, ignore_index=True)

It will be the same output:

  Security Time Vol_21D Vol2_21D MaxAPV_21D MinAPV_21D
0        1    2       3        4          5          6

But I think, more faster and pythonic way: first create an array, then append all raws to array and make data frame from array.

data = []
for i in range(0,5):
    data.append([1,2,3,4,i,6])
df = pd.DataFrame(data, columns=cnames)

I hope it helps.