Mixing
How to perform mathematical operations on all CSV file columns & rows using Pandas
Here is my code:
all_data = pd.read_csv('data2.csv')
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
print(all_data.shape)
print(mu.shape)
print(sigma.shape)
Output:
(20, 24)
(1, 24)
(1, 24)
Sigma and Mu are numpy array or matrix.
I want to perform this operaiton:
all_data = (all_data - mu)/sigma
Here, first column (all rows), of all_data first gets substracted by first column of mu and then divided by first column of sigma
second column (all rows) of all_data first gets substracted by second column of mu and then divided by second column of sigma
Like that
python pandas numpy
asked Nov 19 '18 at 15:59
john doe
203
add a comment |
Here is my code:
all_data = pd.read_csv('data2.csv')
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
print(all_data.shape)
print(mu.shape)
print(sigma.shape)
Output:
(20, 24)
(1, 24)
(1, 24)
Sigma and Mu are numpy array or matrix.
I want to perform this operaiton:
all_data = (all_data - mu)/sigma
Here, first column (all rows), of all_data first gets substracted by first column of mu and then divided by first column of sigma
second column (all rows) of all_data first gets substracted by second column of mu and then divided by second column of sigma
Like that
python pandas numpy
asked Nov 19 '18 at 15:59
john doe
203
add a comment |
Here is my code:
all_data = pd.read_csv('data2.csv')
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
print(all_data.shape)
print(mu.shape)
print(sigma.shape)
Output:
(20, 24)
(1, 24)
(1, 24)
Sigma and Mu are numpy array or matrix.
I want to perform this operaiton:
all_data = (all_data - mu)/sigma
Here, first column (all rows), of all_data first gets substracted by first column of mu and then divided by first column of sigma
second column (all rows) of all_data first gets substracted by second column of mu and then divided by second column of sigma
Like that
python pandas numpy
asked Nov 19 '18 at 15:59
john doe
203
Here is my code:
all_data = pd.read_csv('data2.csv')
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
print(all_data.shape)
print(mu.shape)
print(sigma.shape)
Output:
(20, 24)
(1, 24)
(1, 24)
Sigma and Mu are numpy array or matrix.
I want to perform this operaiton:
all_data = (all_data - mu)/sigma
Here, first column (all rows), of all_data first gets substracted by first column of mu and then divided by first column of sigma
second column (all rows) of all_data first gets substracted by second column of mu and then divided by second column of sigma
Like that
python pandas numpy
python pandas numpy
asked Nov 19 '18 at 15:59
john doe
203
asked Nov 19 '18 at 15:59
john doe
203
asked Nov 19 '18 at 15:59
john doe
203
asked Nov 19 '18 at 15:59
john doe
203
asked Nov 19 '18 at 15:59
john doe
203
203
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
If you work with the underlying numpy arrays of your dataframe, the broadcasting takes care of the work for you:
(all_data.values - mu)/sigma
And put it back in a dataframe with the same columns/index as all_data:
pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
Example:
On this mini dataframe:
all_data = pd.DataFrame(np.random.randint(0,9,(5,5)))
>>> all_data
0 1 2 3 4
0 5 7 1 8 6
1 5 8 0 3 0
2 8 2 0 1 6
3 5 8 7 7 0
4 4 6 0 2 5
With:
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
>>> mu
array([[5.6, 2. , 4. , 4.4, 7.6]])
>>> sigma
array([[1.62480768, 1.26491106, 3.40587727, 2.41660919, 0.48989795]])
You can get:
>>> pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
0 1 2 3 4
0 -0.369274 3.952847 -0.88083 1.489691 -3.265986
1 -0.369274 4.743416 -1.17444 -0.579324 -15.513435
2 1.477098 0.000000 -1.17444 -1.406930 -3.265986
3 -0.369274 4.743416 0.88083 1.075888 -15.513435
4 -0.984732 3.162278 -1.17444 -0.993127 -5.307228
Feel free the check the math, but it satisfies your requirements: the operation is applied on the first column of the dataframe with the first values of sigma and mu, second column with second values, etc...
answered Nov 19 '18 at 16:29
sacul
29.9k41740
add a comment |
how about numpy.matlib.repmat?
df = pd.DataFrame(numpy.random.rand(20, 24))
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
MU = pd.DataFrame(numpy.matlib.repmat(mu,20, 1))
SIGMA = pd.DataFrame(numpy.matlib.repmat(sigma,20, 1))
all_data = (all_data - MU)/SIGMA
answered Nov 19 '18 at 16:35
Robert
33429
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
If you work with the underlying numpy arrays of your dataframe, the broadcasting takes care of the work for you:
(all_data.values - mu)/sigma
And put it back in a dataframe with the same columns/index as all_data:
pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
Example:
On this mini dataframe:
all_data = pd.DataFrame(np.random.randint(0,9,(5,5)))
>>> all_data
0 1 2 3 4
0 5 7 1 8 6
1 5 8 0 3 0
2 8 2 0 1 6
3 5 8 7 7 0
4 4 6 0 2 5
With:
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
>>> mu
array([[5.6, 2. , 4. , 4.4, 7.6]])
>>> sigma
array([[1.62480768, 1.26491106, 3.40587727, 2.41660919, 0.48989795]])
You can get:
>>> pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
0 1 2 3 4
0 -0.369274 3.952847 -0.88083 1.489691 -3.265986
1 -0.369274 4.743416 -1.17444 -0.579324 -15.513435
2 1.477098 0.000000 -1.17444 -1.406930 -3.265986
3 -0.369274 4.743416 0.88083 1.075888 -15.513435
4 -0.984732 3.162278 -1.17444 -0.993127 -5.307228
Feel free the check the math, but it satisfies your requirements: the operation is applied on the first column of the dataframe with the first values of sigma and mu, second column with second values, etc...
answered Nov 19 '18 at 16:29
sacul
29.9k41740
add a comment |
If you work with the underlying numpy arrays of your dataframe, the broadcasting takes care of the work for you:
(all_data.values - mu)/sigma
And put it back in a dataframe with the same columns/index as all_data:
pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
Example:
On this mini dataframe:
all_data = pd.DataFrame(np.random.randint(0,9,(5,5)))
>>> all_data
0 1 2 3 4
0 5 7 1 8 6
1 5 8 0 3 0
2 8 2 0 1 6
3 5 8 7 7 0
4 4 6 0 2 5
With:
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
>>> mu
array([[5.6, 2. , 4. , 4.4, 7.6]])
>>> sigma
array([[1.62480768, 1.26491106, 3.40587727, 2.41660919, 0.48989795]])
You can get:
>>> pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
0 1 2 3 4
0 -0.369274 3.952847 -0.88083 1.489691 -3.265986
1 -0.369274 4.743416 -1.17444 -0.579324 -15.513435
2 1.477098 0.000000 -1.17444 -1.406930 -3.265986
3 -0.369274 4.743416 0.88083 1.075888 -15.513435
4 -0.984732 3.162278 -1.17444 -0.993127 -5.307228
Feel free the check the math, but it satisfies your requirements: the operation is applied on the first column of the dataframe with the first values of sigma and mu, second column with second values, etc...
answered Nov 19 '18 at 16:29
sacul
29.9k41740
add a comment |
If you work with the underlying numpy arrays of your dataframe, the broadcasting takes care of the work for you:
(all_data.values - mu)/sigma
And put it back in a dataframe with the same columns/index as all_data:
pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
Example:
On this mini dataframe:
all_data = pd.DataFrame(np.random.randint(0,9,(5,5)))
>>> all_data
0 1 2 3 4
0 5 7 1 8 6
1 5 8 0 3 0
2 8 2 0 1 6
3 5 8 7 7 0
4 4 6 0 2 5
With:
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
>>> mu
array([[5.6, 2. , 4. , 4.4, 7.6]])
>>> sigma
array([[1.62480768, 1.26491106, 3.40587727, 2.41660919, 0.48989795]])
You can get:
>>> pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
0 1 2 3 4
0 -0.369274 3.952847 -0.88083 1.489691 -3.265986
1 -0.369274 4.743416 -1.17444 -0.579324 -15.513435
2 1.477098 0.000000 -1.17444 -1.406930 -3.265986
3 -0.369274 4.743416 0.88083 1.075888 -15.513435
4 -0.984732 3.162278 -1.17444 -0.993127 -5.307228
Feel free the check the math, but it satisfies your requirements: the operation is applied on the first column of the dataframe with the first values of sigma and mu, second column with second values, etc...
answered Nov 19 '18 at 16:29
sacul
29.9k41740
If you work with the underlying numpy arrays of your dataframe, the broadcasting takes care of the work for you:
(all_data.values - mu)/sigma
And put it back in a dataframe with the same columns/index as all_data:
pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
Example:
On this mini dataframe:
all_data = pd.DataFrame(np.random.randint(0,9,(5,5)))
>>> all_data
0 1 2 3 4
0 5 7 1 8 6
1 5 8 0 3 0
2 8 2 0 1 6
3 5 8 7 7 0
4 4 6 0 2 5
With:
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
>>> mu
array([[5.6, 2. , 4. , 4.4, 7.6]])
>>> sigma
array([[1.62480768, 1.26491106, 3.40587727, 2.41660919, 0.48989795]])
You can get:
>>> pd.DataFrame((all_data.values - mu)/sigma, columns=all_data.columns, index=all_data.index)
0 1 2 3 4
0 -0.369274 3.952847 -0.88083 1.489691 -3.265986
1 -0.369274 4.743416 -1.17444 -0.579324 -15.513435
2 1.477098 0.000000 -1.17444 -1.406930 -3.265986
3 -0.369274 4.743416 0.88083 1.075888 -15.513435
4 -0.984732 3.162278 -1.17444 -0.993127 -5.307228
Feel free the check the math, but it satisfies your requirements: the operation is applied on the first column of the dataframe with the first values of sigma and mu, second column with second values, etc...
answered Nov 19 '18 at 16:29
sacul
29.9k41740
answered Nov 19 '18 at 16:29
sacul
29.9k41740
answered Nov 19 '18 at 16:29
sacul
29.9k41740
answered Nov 19 '18 at 16:29
sacul
29.9k41740
29.9k41740
add a comment |
add a comment |
how about numpy.matlib.repmat?
df = pd.DataFrame(numpy.random.rand(20, 24))
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
MU = pd.DataFrame(numpy.matlib.repmat(mu,20, 1))
SIGMA = pd.DataFrame(numpy.matlib.repmat(sigma,20, 1))
all_data = (all_data - MU)/SIGMA
answered Nov 19 '18 at 16:35
Robert
33429
add a comment |
how about numpy.matlib.repmat?
df = pd.DataFrame(numpy.random.rand(20, 24))
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
MU = pd.DataFrame(numpy.matlib.repmat(mu,20, 1))
SIGMA = pd.DataFrame(numpy.matlib.repmat(sigma,20, 1))
all_data = (all_data - MU)/SIGMA
answered Nov 19 '18 at 16:35
Robert
33429
add a comment |
how about numpy.matlib.repmat?
df = pd.DataFrame(numpy.random.rand(20, 24))
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
MU = pd.DataFrame(numpy.matlib.repmat(mu,20, 1))
SIGMA = pd.DataFrame(numpy.matlib.repmat(sigma,20, 1))
all_data = (all_data - MU)/SIGMA
answered Nov 19 '18 at 16:35
Robert
33429
how about numpy.matlib.repmat?
df = pd.DataFrame(numpy.random.rand(20, 24))
mu = np.array([all_data.mean(0)])
sigma = np.array([np.std(all_data,axis=0)])
MU = pd.DataFrame(numpy.matlib.repmat(mu,20, 1))
SIGMA = pd.DataFrame(numpy.matlib.repmat(sigma,20, 1))
all_data = (all_data - MU)/SIGMA
answered Nov 19 '18 at 16:35
Robert
33429
answered Nov 19 '18 at 16:35
Robert
33429
answered Nov 19 '18 at 16:35
Robert
33429
answered Nov 19 '18 at 16:35
Robert
33429
33429
add a comment |
add a comment |
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