从numpy数组创建Panda Df

Question

I am running a np.random.choice like the one below.

record = np.random.choice(data, size=6, p=prob)
        maxv = max(record)
        minv = min(record)
        val = record

From this I am finding the min and the max. I want to join this to an pandas dataframe. Below is my desired output:

Min,Max,value
1,5,2
1,5,3
1,5,3
1,5,5
1,5,1
1,5,3

This is an example of the output I would like from one simulation. Keep in mind I am performing this simulation many times so I would like to continuously be able to add onto the dataframe that is created. Each simulation will have its own min and max respectively. I also would like to keep the min and max in the output (why 1 and 5 are in the example output).

Answer 1

I'd create the df with the initial data column 'Val' and then just add the new columns in a one liner:

In [242]:
df = pd.DataFrame({'Val':np.random.randint(1,6,6)})
df['Min'], df['Max'] = df['Val'].min(), df['Val'].max()
df

Out[242]:
   Val  Min  Max
0    4    2    5
1    5    2    5
2    5    2    5
3    4    2    5
4    5    2    5
5    2    2    5

Answer 2

This is how I solve it:

record = np.random.choice(data, size=6, p=prob)
maxv = [max(record)] * len(record)
minv = [min(record)] * len(record)

new_data = zip(minv, maxv, record)

df = DataFrame(new_data, columns=['Min', 'Max', 'val'])

Answer 3

Simply iterate through simulation and append values into dataframe:

# CREATE DATA FRAME STRUCTURE
df = pd.DataFrame(columns=['Min', 'Max', 'val'])

# RUN SIMULATION IN LOOP ITERATION
record = np.random.choice(data, size=6, p=prob)

for i in range(len(record)):
    maxv = np.max(record)
    minv = np.min(record)
    val = record[i]   

    # APPEND ROW
    df.loc[len(df)] = [maxv, minv, val]