I have a 1x300 numpy array from my Word2Vec model which is returns like this:
[ -2.55022556e-01 1.06162608e+00 -5.86191297e-01 -4.43067521e-01
4.46810514e-01 4.31743741e-01 2.16610283e-01 9.27684903e-01
-4.47879761e-01 -9.11142007e-02 3.27048987e-01 -8.05553675e-01
-8.54483843e-02 -2.85595834e-01 -2.70745698e-02 -3.08014955e-02
1.53204888e-01 3.16114485e-01 -2.82659411e-01 -2.98218042e-01
-1.03240972e-02 2.12806061e-01 1.63605273e-01 9.42423999e-01
1.20789325e+00 4.11570221e-01 -5.46323597e-01 1.95108235e-01
-4.53743488e-01 -1.28625661e-01 -7.43277609e-01 1.11551750e+00
-4.51873302e-01 -1.14495361e+00 -6.69551417e-02 6.88364863e-01
-6.01781428e-01 -2.36386538e-01 -3.64305973e-01 1.18274912e-01
2.03438237e-01 -1.01153564e+00 6.67958856e-01 1.80363625e-01
1.26524955e-01 -2.96024203e-01 -9.93479714e-02 -4.93405871e-02
1.02504417e-01 7.63318688e-02 -3.68398607e-01 3.03587675e-01
-2.90227026e-01 1.51891649e-01 -6.93689287e-03 -3.99766594e-01
-1.86124116e-01 -2.86920428e-01 2.04880714e-01 1.39914978e+00
1.84370011e-01 -4.58923727e-01 3.91094625e-01 -7.52937734e-01
3.05261135e-01 -4.55163687e-01 7.22679734e-01 -3.76093656e-01
6.05900526e-01 3.26470852e-01 4.72957864e-02 -1.18182398e-01
3.51043999e-01 -3.07209432e-01 -6.10330477e-02 4.14131492e-01
7.57511556e-02 -6.48704231e-01 1.42518353e+00 -9.20495167e-02
6.36665523e-01 5.48510313e-01 5.92754841e-01 -6.29535854e-01
-4.47180003e-01 -8.99413109e-01 -1.52441502e-01 -1.98326513e-01
4.74154204e-01 -2.07036674e-01 -6.70400202e-01 6.67807996e-01
-1.04234733e-01 7.16163218e-01 3.32825005e-01 8.20083246e-02
5.88186264e-01 4.06852067e-01 2.66174138e-01 -5.35981596e-01
3.26077454e-02 -4.04357493e-01 2.19569445e-01 -2.74264365e-01
-1.65187627e-01 -4.06753153e-01 6.12065434e-01 -1.89857081e-01
-5.56927800e-01 -6.78636551e-01 -7.52498448e-01 1.04564428e+00
5.32510102e-01 5.05628288e-01 1.95120305e-01 -6.40793025e-01
5.73082231e-02 -1.58281475e-02 -2.62718409e-01 1.74351722e-01
-6.95129633e-02 3.44214857e-01 -4.24746841e-01 -2.75907904e-01
-6.60992935e-02 -1.19041657e+00 -6.01056278e-01 5.67718685e-01
-6.47478551e-02 1.55902460e-01 -2.48480186e-01 5.56753576e-01
1.29889056e-01 3.91534269e-01 1.28707469e-01 1.29670590e-01
-6.98880851e-01 2.43386969e-01 7.70289376e-02 -1.14947490e-01
-4.31593180e-01 -6.16873622e-01 6.03831768e-01 -2.07050622e-01
1.23276520e+00 -1.67524610e-02 -4.67656374e-01 1.00281858e+00
5.17916441e-01 -7.99495637e-01 -4.22653735e-01 -1.45487636e-01
-8.71369673e-04 1.25453219e-01 -1.25869447e-02 4.66426492e-01
5.07026255e-01 -6.53024793e-01 7.53435045e-02 8.33864748e-01
3.37398499e-01 7.50920832e-01 -4.80326146e-01 -4.52838868e-01
5.92808545e-01 -3.57870340e-01 -1.07011057e-01 -1.13945460e+00
3.97635132e-01 1.23554178e-01 4.81683850e-01 5.47445454e-02
-2.18614921e-01 -2.00085923e-01 -3.73975009e-01 8.74632657e-01
6.71471596e-01 -4.01738763e-01 4.76147681e-01 -5.79257011e-01
-1.51511624e-01 1.43170074e-01 5.00052273e-01 1.46719962e-01
2.43085429e-01 5.89158475e-01 -5.25088668e-01 -2.65306592e-01
2.18211919e-01 3.83228660e-01 -2.51622144e-02 2.32621357e-01
8.06669474e-01 1.37254462e-01 4.59401071e-01 5.63044667e-01
-5.79878241e-02 2.68106610e-01 5.47239482e-01 -5.05441546e-01]
It's so frustrating to read because I just want to get a 1x2 array like [12,19] so I can represent it to graph and make a cosine distance measurement to the 1x2 array.
How to do it? Or how to represent the 1x300 Word2Vec model to a 2D graph?
There are many ways to apply "dimensionality reduction" to high-dimensional data, for aid in interpretation or graphing.
One super-simple way to reduce your 300-dimensions to just 2-dimensions, for plotting on a flat screen/paper: just discard 298 of the dimensions! You'll have something to plot – such as the point (-0.255022556, 1.06162608) if taking just the 1st 2 dimensions of your example vector.
However, starting from word2vec vectors, those won't likely be very interesting points, individually or when you start plotting multiple words. The exact axes dimensions of such vectors are unlikely to be intuitively meaningful to humans, and you're throwing 99.7% of all the meaning per vector away – and quite likely the dimensions which (in concert with each other) capture semantically-meaningful relationships.
So you'd be more likely to do some more thoughtful dimensionality-reduction. A super-simple technique would be to pick two vector-directions that are thought to be meaningful as your new X and Y axes. In the word2vec world, these wouldn't necessarily be existing vectors in the set – though they could be – but might be the difference between two vectors. (The analogy-solving power of word2vec vectors essentially comes from discovering the difference-between two vectors A and B, then applying that difference to a third vector C to find a 4th vector D, at which point D often has the same human-intuitive analogical-relationship to C as B had to A.)
For example, you might difference the word-vectors for 'man' and 'woman', to get a vector which bootstraps your new X-axis. Then difference the word-vectors for 'parent' and 'worker', to get vector which bootstraps your new Y-axis. Then, for every candidate 300-dimensional vector you want to plot, find that candidate vector's "new X" by calculating the magnitude of its projection onto your X-direction-vector. Then, find that candidate vector's "new Y" by calculating the magnitude of its projection onto your Y-direction-vector. This might result in a set of relative values that, on a 2-D chart, vaguely match human intuitions about often-observed linguisti relationships between gender and familial/workplace roles.
As @poorna-prudhvi's comment mentions, PCA and t-SNE are other techniques – which may specifically do better at preserving certain interesting qualities of the full-dimensional data. t-SNE, especially, was invented for to support machine-learning and plotting, and tries to keep the distance-relationships that existed in the higher-number-of-dimensions similar in the lower-number-of-dimensions.