Reputation:
I need to calculate statistics for each node of a 2D grid. I figured the easy way to do this was to take the cross join (AKA cartesian product) of two ranges. I implemented this using numpy
as this function:
def node_grid(x_range, y_range, x_increment, y_increment):
x_min = float(x_range[0])
x_max = float(x_range[1])
x_num = (x_max - x_min)/x_increment + 1
y_min = float(y_range[0])
y_max = float(y_range[1])
y_num = (y_max - y_min)/y_increment + 1
x = np.linspace(x_min, x_max, x_num)
y = np.linspace(y_min, y_max, y_num)
ng = list(product(x, y))
ng = np.array(ng)
return ng, x, y
However when I convert this to a pandas
dataframe it drops values. For example:
In [2]: ng = node_grid(x_range=(-60, 120), y_range=(0, 40), x_increment=0.1, y_increment=0.1)
In [3]: ng[0][(ng[0][:,0] > -31) & (ng[0][:,0] < -30) & (ng[0][:,1]==10)]
Out[3]: array([[-30.9, 10. ],
[-30.8, 10. ],
[-30.7, 10. ],
[-30.6, 10. ],
[-30.5, 10. ],
[-30.4, 10. ],
[-30.3, 10. ],
[-30.2, 10. ],
[-30.1, 10. ]])
In [4]: node_df = pd.DataFrame(ng[0])
node_df.columns = ['xx','depth']
print(node_df[(node_df.depth==10) & node_df.xx.between(-30,-31)])
Out[4]:Empty DataFrame
Columns: [xx, depth]
Index: []
The dataframe isn't empty:
In [5]: print(node_df.head())
Out[5]: xx depth
0 -60.0 0.0
1 -60.0 0.1
2 -60.0 0.2
3 -60.0 0.3
4 -60.0 0.4
values from the numpy array are being dropped when they are being put into the pandas array. Why?
Upvotes: 2
Views: 222
Reputation: 16
the "between" function demands that the first argument be less than the latter.
In: print(node_df[(node_df.depth==10) & node_df.xx.between(-31,-30)])
xx depth
116390 -31.0 10.0
116791 -30.9 10.0
117192 -30.8 10.0
117593 -30.7 10.0
117994 -30.6 10.0
118395 -30.5 10.0
118796 -30.4 10.0
119197 -30.3 10.0
119598 -30.2 10.0
119999 -30.1 10.0
120400 -30.0 10.0
For clarity the product()
function used comes from the itertools
package, i.e., from itertools import product
Upvotes: 0
Reputation: 523
I can't fully reproduce your code.
But I find the problem is that you have to turn the lower and upper boundaries around in the between
query. The following works for me:
print(node_df[(node_df.depth==10) & node_df.xx.between(-31,-30)])
when using:
ng = np.array([[-30.9, 10. ],
[-30.8, 10. ],
[-30.7, 10. ],
[-30.6, 10. ],
[-30.5, 10. ],
[-30.4, 10. ],
[-30.3, 10. ],
[-30.2, 10. ],
[-30.1, 10. ]])
node_df = pd.DataFrame(ng)
Upvotes: 0