The value that you want can be computed with the isf
(inverse survival function) method of the scipy.stats.chi2
distribution.
This method uses broadcasting, so you can create your table with just a couple lines of code:
In [61]: from scipy.stats import chi2In [62]: p = np.array([0.995, 0.99, 0.975, 0.95, 0.90, 0.10, 0.05, 0.025, 0.01, 0.005])
Make df
an array with shape (n, 1)
, so it broadcasts with p
to create a 2-d array of all the pairings:
In [63]: df = np.array(range(1, 30) + range(30, 101, 10)).reshape(-1, 1)
Now just call isf
:
In [64]: table = chi2.isf(p, df)
Tweak the default print options of numpy to create a nicely formatted table:
In [65]: np.set_printoptions(linewidth=130, formatter=dict(float=lambda x: "%7.3f" % x))In [66]: table
Out[66]:
array([[ 0.000, 0.000, 0.001, 0.004, 0.016, 2.706, 3.841, 5.024, 6.635, 7.879],[ 0.010, 0.020, 0.051, 0.103, 0.211, 4.605, 5.991, 7.378, 9.210, 10.597],[ 0.072, 0.115, 0.216, 0.352, 0.584, 6.251, 7.815, 9.348, 11.345, 12.838],[ 0.207, 0.297, 0.484, 0.711, 1.064, 7.779, 9.488, 11.143, 13.277, 14.860],[ 0.412, 0.554, 0.831, 1.145, 1.610, 9.236, 11.070, 12.833, 15.086, 16.750],[ 0.676, 0.872, 1.237, 1.635, 2.204, 10.645, 12.592, 14.449, 16.812, 18.548],[ 0.989, 1.239, 1.690, 2.167, 2.833, 12.017, 14.067, 16.013, 18.475, 20.278],[ 1.344, 1.646, 2.180, 2.733, 3.490, 13.362, 15.507, 17.535, 20.090, 21.955],[ 1.735, 2.088, 2.700, 3.325, 4.168, 14.684, 16.919, 19.023, 21.666, 23.589],[ 2.156, 2.558, 3.247, 3.940, 4.865, 15.987, 18.307, 20.483, 23.209, 25.188],[ 2.603, 3.053, 3.816, 4.575, 5.578, 17.275, 19.675, 21.920, 24.725, 26.757],[ 3.074, 3.571, 4.404, 5.226, 6.304, 18.549, 21.026, 23.337, 26.217, 28.300],[ 3.565, 4.107, 5.009, 5.892, 7.042, 19.812, 22.362, 24.736, 27.688, 29.819],[ 4.075, 4.660, 5.629, 6.571, 7.790, 21.064, 23.685, 26.119, 29.141, 31.319],[ 4.601, 5.229, 6.262, 7.261, 8.547, 22.307, 24.996, 27.488, 30.578, 32.801],[ 5.142, 5.812, 6.908, 7.962, 9.312, 23.542, 26.296, 28.845, 32.000, 34.267],[ 5.697, 6.408, 7.564, 8.672, 10.085, 24.769, 27.587, 30.191, 33.409, 35.718],[ 6.265, 7.015, 8.231, 9.390, 10.865, 25.989, 28.869, 31.526, 34.805, 37.156],[ 6.844, 7.633, 8.907, 10.117, 11.651, 27.204, 30.144, 32.852, 36.191, 38.582],[ 7.434, 8.260, 9.591, 10.851, 12.443, 28.412, 31.410, 34.170, 37.566, 39.997],[ 8.034, 8.897, 10.283, 11.591, 13.240, 29.615, 32.671, 35.479, 38.932, 41.401],[ 8.643, 9.542, 10.982, 12.338, 14.041, 30.813, 33.924, 36.781, 40.289, 42.796],[ 9.260, 10.196, 11.689, 13.091, 14.848, 32.007, 35.172, 38.076, 41.638, 44.181],[ 9.886, 10.856, 12.401, 13.848, 15.659, 33.196, 36.415, 39.364, 42.980, 45.559],[ 10.520, 11.524, 13.120, 14.611, 16.473, 34.382, 37.652, 40.646, 44.314, 46.928],[ 11.160, 12.198, 13.844, 15.379, 17.292, 35.563, 38.885, 41.923, 45.642, 48.290],[ 11.808, 12.879, 14.573, 16.151, 18.114, 36.741, 40.113, 43.195, 46.963, 49.645],[ 12.461, 13.565, 15.308, 16.928, 18.939, 37.916, 41.337, 44.461, 48.278, 50.993],[ 13.121, 14.256, 16.047, 17.708, 19.768, 39.087, 42.557, 45.722, 49.588, 52.336],[ 13.787, 14.953, 16.791, 18.493, 20.599, 40.256, 43.773, 46.979, 50.892, 53.672],[ 20.707, 22.164, 24.433, 26.509, 29.051, 51.805, 55.758, 59.342, 63.691, 66.766],[ 27.991, 29.707, 32.357, 34.764, 37.689, 63.167, 67.505, 71.420, 76.154, 79.490],[ 35.534, 37.485, 40.482, 43.188, 46.459, 74.397, 79.082, 83.298, 88.379, 91.952],[ 43.275, 45.442, 48.758, 51.739, 55.329, 85.527, 90.531, 95.023, 100.425, 104.215],[ 51.172, 53.540, 57.153, 60.391, 64.278, 96.578, 101.879, 106.629, 112.329, 116.321],[ 59.196, 61.754, 65.647, 69.126, 73.291, 107.565, 113.145, 118.136, 124.116, 128.299],[ 67.328, 70.065, 74.222, 77.929, 82.358, 118.498, 124.342, 129.561, 135.807, 140.169]])
By setting the print options, the output shows only three decimal places, but the actual full values are still in table. E.g.:
In [67]: table[0, 0]
Out[67]: 3.927042222052108e-05In [68]: table[0, 8]
Out[68]: 6.6348966010212171