Write python simulation output to a matrix
I am trying to take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. I want this to give me 1000 rows each with different results, for each column.
I essentially want to say something like the following:
for i in range(1000):
np.sum(df['A']) = iterations[i, j]
where df['A'] is one of the columns I want to sum for each iteration. That is, 'for each iteration, sum the column values and 'place' this result in a new dataframe called 'iterations', specifying where the result is going to go'. I understand the code doesn't make sense but it describes what I am trying to achieve. To be clear, I do not want to write the result to a csv or txt file.
Thank you in advance for your advice.
pandas numpy
asked Nov 13 '18 at 17:37
EarlofMarEarlofMar
276
add a comment |
I am trying to take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. I want this to give me 1000 rows each with different results, for each column.
I essentially want to say something like the following:
for i in range(1000):
np.sum(df['A']) = iterations[i, j]
where df['A'] is one of the columns I want to sum for each iteration. That is, 'for each iteration, sum the column values and 'place' this result in a new dataframe called 'iterations', specifying where the result is going to go'. I understand the code doesn't make sense but it describes what I am trying to achieve. To be clear, I do not want to write the result to a csv or txt file.
Thank you in advance for your advice.
pandas numpy
asked Nov 13 '18 at 17:37
EarlofMarEarlofMar
276
add a comment |
I am trying to take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. I want this to give me 1000 rows each with different results, for each column.
I essentially want to say something like the following:
for i in range(1000):
np.sum(df['A']) = iterations[i, j]
where df['A'] is one of the columns I want to sum for each iteration. That is, 'for each iteration, sum the column values and 'place' this result in a new dataframe called 'iterations', specifying where the result is going to go'. I understand the code doesn't make sense but it describes what I am trying to achieve. To be clear, I do not want to write the result to a csv or txt file.
Thank you in advance for your advice.
pandas numpy
asked Nov 13 '18 at 17:37
EarlofMarEarlofMar
276
I am trying to take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. I want this to give me 1000 rows each with different results, for each column.
I essentially want to say something like the following:
for i in range(1000):
np.sum(df['A']) = iterations[i, j]
where df['A'] is one of the columns I want to sum for each iteration. That is, 'for each iteration, sum the column values and 'place' this result in a new dataframe called 'iterations', specifying where the result is going to go'. I understand the code doesn't make sense but it describes what I am trying to achieve. To be clear, I do not want to write the result to a csv or txt file.
Thank you in advance for your advice.
pandas numpy
pandas numpy
asked Nov 13 '18 at 17:37
EarlofMarEarlofMar
276
asked Nov 13 '18 at 17:37
EarlofMarEarlofMar
276
asked Nov 13 '18 at 17:37
EarlofMarEarlofMar
276
asked Nov 13 '18 at 17:37
EarlofMarEarlofMar
276
asked Nov 13 '18 at 17:37
EarlofMarEarlofMar
276
276
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
Take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. This should give me 1000 rows each with different results, for each column. We can write:
import os
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
df_output =
for i in tqdm(range(1000)):
sample_matrix = np.random.rand(60,4)
df = pd.DataFrame(sample_matrix)
df.columns = ['V_' + str(col) for col in df.columns]
df_output.append(np.array(df.sum()))
df_output
df_output will be a matrix, where the number of rows is 1000 (= the number of simulations)
answered Nov 13 '18 at 18:15
kon_ukon_u
1966
add a comment |
Without knowing how/why you plan on randomizing each column each iteration, this will work:
df = pd.DataFrame(np.random.rand(500,4)) # initialize with random data
iterations = [df.sum()]
for i in range(999):
iterations = np.vstack([iterations, df.sum()])
iterations = pd.DataFrame(iterations)
answered Nov 13 '18 at 21:51
Brian JosephBrian Joseph
5109
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. This should give me 1000 rows each with different results, for each column. We can write:
import os
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
df_output =
for i in tqdm(range(1000)):
sample_matrix = np.random.rand(60,4)
df = pd.DataFrame(sample_matrix)
df.columns = ['V_' + str(col) for col in df.columns]
df_output.append(np.array(df.sum()))
df_output
df_output will be a matrix, where the number of rows is 1000 (= the number of simulations)
answered Nov 13 '18 at 18:15
kon_ukon_u
1966
add a comment |
Take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. This should give me 1000 rows each with different results, for each column. We can write:
import os
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
df_output =
for i in tqdm(range(1000)):
sample_matrix = np.random.rand(60,4)
df = pd.DataFrame(sample_matrix)
df.columns = ['V_' + str(col) for col in df.columns]
df_output.append(np.array(df.sum()))
df_output
df_output will be a matrix, where the number of rows is 1000 (= the number of simulations)
answered Nov 13 '18 at 18:15
kon_ukon_u
1966
add a comment |
Take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. This should give me 1000 rows each with different results, for each column. We can write:
import os
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
df_output =
for i in tqdm(range(1000)):
sample_matrix = np.random.rand(60,4)
df = pd.DataFrame(sample_matrix)
df.columns = ['V_' + str(col) for col in df.columns]
df_output.append(np.array(df.sum()))
df_output
df_output will be a matrix, where the number of rows is 1000 (= the number of simulations)
answered Nov 13 '18 at 18:15
kon_ukon_u
1966
Take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. This should give me 1000 rows each with different results, for each column. We can write:
import os
import pandas as pd
import numpy as np
import random
from tqdm import tqdm
df_output =
for i in tqdm(range(1000)):
sample_matrix = np.random.rand(60,4)
df = pd.DataFrame(sample_matrix)
df.columns = ['V_' + str(col) for col in df.columns]
df_output.append(np.array(df.sum()))
df_output
df_output will be a matrix, where the number of rows is 1000 (= the number of simulations)
answered Nov 13 '18 at 18:15
kon_ukon_u
1966
answered Nov 13 '18 at 18:15
kon_ukon_u
1966
answered Nov 13 '18 at 18:15
kon_ukon_u
1966
answered Nov 13 '18 at 18:15
kon_ukon_u
1966
1966
add a comment |
add a comment |
Without knowing how/why you plan on randomizing each column each iteration, this will work:
df = pd.DataFrame(np.random.rand(500,4)) # initialize with random data
iterations = [df.sum()]
for i in range(999):
iterations = np.vstack([iterations, df.sum()])
iterations = pd.DataFrame(iterations)
answered Nov 13 '18 at 21:51
Brian JosephBrian Joseph
5109
add a comment |
Without knowing how/why you plan on randomizing each column each iteration, this will work:
df = pd.DataFrame(np.random.rand(500,4)) # initialize with random data
iterations = [df.sum()]
for i in range(999):
iterations = np.vstack([iterations, df.sum()])
iterations = pd.DataFrame(iterations)
answered Nov 13 '18 at 21:51
Brian JosephBrian Joseph
5109
add a comment |
Without knowing how/why you plan on randomizing each column each iteration, this will work:
df = pd.DataFrame(np.random.rand(500,4)) # initialize with random data
iterations = [df.sum()]
for i in range(999):
iterations = np.vstack([iterations, df.sum()])
iterations = pd.DataFrame(iterations)
answered Nov 13 '18 at 21:51
Brian JosephBrian Joseph
5109
Without knowing how/why you plan on randomizing each column each iteration, this will work:
df = pd.DataFrame(np.random.rand(500,4)) # initialize with random data
iterations = [df.sum()]
for i in range(999):
iterations = np.vstack([iterations, df.sum()])
iterations = pd.DataFrame(iterations)
answered Nov 13 '18 at 21:51
Brian JosephBrian Joseph
5109
answered Nov 13 '18 at 21:51
Brian JosephBrian Joseph
5109
answered Nov 13 '18 at 21:51
Brian JosephBrian Joseph
5109
answered Nov 13 '18 at 21:51
Brian JosephBrian Joseph
5109
5109
add a comment |
add a comment |
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