I am trying to create a matrix / DataFrame with the numbers stored in 2 variables
x = np.linspace(0,50)
y = np.exp(x)
and I would like them to look like this:
x | y
___________________
0 | 1.0...
1 | 2.77...
2 | 7.6...
... | ...
50 | 5.18e+21...
I would like it to be in a DataFrame so I can work with it with the pandas library.
Thanks in advance
With pandas:
You can issue
>>> xs = np.arange(51)
>>> ys = np.exp(xs)
to get the x and y values and then build your dataframe with
>>> df = pd.DataFrame({'x': xs, 'y': ys})
>>> df
x y
0 0 1.000000e+00
1 1 2.718282e+00
2 2 7.389056e+00
3 3 2.008554e+01
...
In this case, you can also use the x-values as the index of a series without losing any information.
>>> index = pd.RangeIndex(0, 51, name='x')
>>> exps = pd.Series(data=np.exp(index), index=index, name='y')
>>> exps
x
0 1.000000e+00
1 2.718282e+00
2 7.389056e+00
3 2.008554e+01
...
Name: y, dtype: float64
Without pandas:
Consider if you truly need a dataframe or series. You could just leave it at
>>> xs = np.arange(51)
>>> ys = np.exp(xs)
and then index into ys with the integers 0, 1, 2, ... to get the values of exp(0), exp(1), exp(2), ...
import pandas as pd
df = pd.DataFrame({'x':x, 'y':y})
Change the key in the dictionary to your desired column name.
Assign column names and set columns in the mean time :
import pandas as pd
df = pd.DataFrame({"x" : x , "y" : y})
You can do the following.
import pandas as pd
import numpy as np
df = pd.DataFrame()
df['x'] = np.linspace(0,50)
df['y'] = np.exp(df['x'])
Simply:
Code:
import pandas as pd
import numpy as np
x = np.linspace(0,50)
y = np.exp(x)
df = pd.DataFrame({'x': x, 'y': y})
Just make a list of tuples and pass it to the DataFrame constructor:
df = pd.DataFrame([(i, np.exp(i)) for i in np.linspace(0,50)], columns=['x', 'y'])
Output
x y
0 1.000000e+00
1 2.718282e+00
2 7.389056e+00
...
np.arange(50) does not produce the same output as np.linspace(0, 50).What you are looking for is [np.concatenate][1].
So for your example, the code would be
import numpy as np
x = np.linspace(0,50)
y = np.exp(x)
z = np.concatenate((x.reshape(1,-1),y.reshape(1,-1))).T
print(z.shape)
# (2,50)
