How can I add multiple unique random values to a Pandas DataFrame efficiently?
I have a string of length 20, with about 30% 0's and 70% 1's. So something like this '11101001110111011110'
I would like to generate 10 more strings with the same 1, 0 distribution.
Right now I can do this by calling
''.join(map(str, np.random.choice([0, 1], size=20, p=[0.3, 0.7])))
10 times.
However, for efficiency reasons, is it possible for me to call random.choice as few times as possible, perhaps once?
Right now I'm accomplishing this by creating a dataframe and then adding a column that calls the above functions, like this.
df = pd.DataFrame([None]*10)
df['Stuff'] = ''.join(map(str, np.random.choice([0, 1], size=20, p=[0.3,
0.7])))
df
However, each of the 10 values are the same.
How do I make them unique randomized values?
If not is there some way to 'vectorize' the np.random function so my process of generating these random numbers can be more efficient?
Thanks!
python pandas
asked Nov 25 '18 at 3:43
CalculasiansCalculasians
42
add a comment |
I have a string of length 20, with about 30% 0's and 70% 1's. So something like this '11101001110111011110'
I would like to generate 10 more strings with the same 1, 0 distribution.
Right now I can do this by calling
''.join(map(str, np.random.choice([0, 1], size=20, p=[0.3, 0.7])))
10 times.
However, for efficiency reasons, is it possible for me to call random.choice as few times as possible, perhaps once?
Right now I'm accomplishing this by creating a dataframe and then adding a column that calls the above functions, like this.
df = pd.DataFrame([None]*10)
df['Stuff'] = ''.join(map(str, np.random.choice([0, 1], size=20, p=[0.3,
0.7])))
df
However, each of the 10 values are the same.
How do I make them unique randomized values?
If not is there some way to 'vectorize' the np.random function so my process of generating these random numbers can be more efficient?
Thanks!
python pandas
asked Nov 25 '18 at 3:43
CalculasiansCalculasians
42
add a comment |
I have a string of length 20, with about 30% 0's and 70% 1's. So something like this '11101001110111011110'
I would like to generate 10 more strings with the same 1, 0 distribution.
Right now I can do this by calling
''.join(map(str, np.random.choice([0, 1], size=20, p=[0.3, 0.7])))
10 times.
However, for efficiency reasons, is it possible for me to call random.choice as few times as possible, perhaps once?
Right now I'm accomplishing this by creating a dataframe and then adding a column that calls the above functions, like this.
df = pd.DataFrame([None]*10)
df['Stuff'] = ''.join(map(str, np.random.choice([0, 1], size=20, p=[0.3,
0.7])))
df
However, each of the 10 values are the same.
How do I make them unique randomized values?
If not is there some way to 'vectorize' the np.random function so my process of generating these random numbers can be more efficient?
Thanks!
python pandas
asked Nov 25 '18 at 3:43
CalculasiansCalculasians
42
I have a string of length 20, with about 30% 0's and 70% 1's. So something like this '11101001110111011110'
I would like to generate 10 more strings with the same 1, 0 distribution.
Right now I can do this by calling
''.join(map(str, np.random.choice([0, 1], size=20, p=[0.3, 0.7])))
10 times.
However, for efficiency reasons, is it possible for me to call random.choice as few times as possible, perhaps once?
Right now I'm accomplishing this by creating a dataframe and then adding a column that calls the above functions, like this.
df = pd.DataFrame([None]*10)
df['Stuff'] = ''.join(map(str, np.random.choice([0, 1], size=20, p=[0.3,
0.7])))
df
However, each of the 10 values are the same.
How do I make them unique randomized values?
If not is there some way to 'vectorize' the np.random function so my process of generating these random numbers can be more efficient?
Thanks!
python pandas
python pandas
asked Nov 25 '18 at 3:43
CalculasiansCalculasians
42
asked Nov 25 '18 at 3:43
CalculasiansCalculasians
42
asked Nov 25 '18 at 3:43
CalculasiansCalculasians
42
asked Nov 25 '18 at 3:43
CalculasiansCalculasians
42
asked Nov 25 '18 at 3:43
CalculasiansCalculasians
42
42
add a comment |
add a comment |
1 Answer
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The problem with your example is that you assign a single value to the whole df. You could just build a list comprehension for this.
l1 = [str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for i in range(len(df.index))]
df = df.assign(Stuff=l1)
Other way to make this work out is by creating the values in a numpy array, like:
array = np.fromiter((str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for x in range(10)), dtype=float)
If you want, you could look for the dtypes for this, since the string type raises an error. These are the ways I could figure it out. Good luck!
edited Nov 25 '18 at 8:01
dataLeo
6031419
answered Nov 25 '18 at 5:07
Pedro MorescoPedro Moresco
214
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The problem with your example is that you assign a single value to the whole df. You could just build a list comprehension for this.
l1 = [str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for i in range(len(df.index))]
df = df.assign(Stuff=l1)
Other way to make this work out is by creating the values in a numpy array, like:
array = np.fromiter((str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for x in range(10)), dtype=float)
If you want, you could look for the dtypes for this, since the string type raises an error. These are the ways I could figure it out. Good luck!
edited Nov 25 '18 at 8:01
dataLeo
6031419
answered Nov 25 '18 at 5:07
Pedro MorescoPedro Moresco
214
add a comment |
The problem with your example is that you assign a single value to the whole df. You could just build a list comprehension for this.
l1 = [str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for i in range(len(df.index))]
df = df.assign(Stuff=l1)
Other way to make this work out is by creating the values in a numpy array, like:
array = np.fromiter((str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for x in range(10)), dtype=float)
If you want, you could look for the dtypes for this, since the string type raises an error. These are the ways I could figure it out. Good luck!
edited Nov 25 '18 at 8:01
dataLeo
6031419
answered Nov 25 '18 at 5:07
Pedro MorescoPedro Moresco
214
add a comment |
The problem with your example is that you assign a single value to the whole df. You could just build a list comprehension for this.
l1 = [str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for i in range(len(df.index))]
df = df.assign(Stuff=l1)
Other way to make this work out is by creating the values in a numpy array, like:
array = np.fromiter((str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for x in range(10)), dtype=float)
If you want, you could look for the dtypes for this, since the string type raises an error. These are the ways I could figure it out. Good luck!
edited Nov 25 '18 at 8:01
dataLeo
6031419
answered Nov 25 '18 at 5:07
Pedro MorescoPedro Moresco
214
The problem with your example is that you assign a single value to the whole df. You could just build a list comprehension for this.
l1 = [str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for i in range(len(df.index))]
df = df.assign(Stuff=l1)
Other way to make this work out is by creating the values in a numpy array, like:
array = np.fromiter((str(np.random.choice([0, 1], size = 20, p = [0.3, 0.7])) for x in range(10)), dtype=float)
If you want, you could look for the dtypes for this, since the string type raises an error. These are the ways I could figure it out. Good luck!
edited Nov 25 '18 at 8:01
dataLeo
6031419
answered Nov 25 '18 at 5:07
Pedro MorescoPedro Moresco
214
edited Nov 25 '18 at 8:01
dataLeo
6031419
edited Nov 25 '18 at 8:01
dataLeo
6031419
edited Nov 25 '18 at 8:01
dataLeo
6031419
6031419
answered Nov 25 '18 at 5:07
Pedro MorescoPedro Moresco
214
answered Nov 25 '18 at 5:07
Pedro MorescoPedro Moresco
214
answered Nov 25 '18 at 5:07
Pedro MorescoPedro Moresco
214
214
add a comment |
add a comment |
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