move python code snippets to python folder

python/birth-rate.py

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+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+
+sns.set()
+
+births = pd.read_csv("data/births.csv")
+births["decade"] = 10 * (births["year"] // 10)
+
+
+births.pivot_table("births", index="year", columns="gender", aggfunc="sum").plot()
+plt.ylabel("total births per year")
+
+plt.show(block=True)

python/ds-026.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import seaborn
+import time
+
+seaborn.set()
+
+rainfall = pd.read_csv(
+    "https://raw.githubusercontent.com/jakevdp/PythonDataScienceHandbook/master/notebooks/data/Seattle2014.csv"
+)["TMAX"].values
+print(rainfall)
+# inches = rainfall / 254.0  # 1/10mm -> inches
+rainfall.shape
+plt.hist(rainfall, 40)
+plt.show(block=True)

python/hello.py

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+# name = "John"
+# print("Hello, %s \n !" % name, "555")
+
+# a = 1.2345
+
+# print("%.2f" % a)
+
+# b = 1245
+
+# print("%x/%X" % (b, b))
+
+# data = ["John", "Doe", 53.44]
+# print(data[2])
+
+# # reverse a string
+
+# astring = "Hello world!"
+# print(astring[::-1])
+
+# m = True
+# if m != True:
+#     print("hahah")
+# else:
+#     print("wooooo")
+
+for i in range(10):
+    print(i)

python/hist.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+# data = np.random.randn(1000)
+# plt.hist(data, bins=50)
+# plt.show()
+
+mean = [0, 0]
+cov = [[1, 1], [1, 2]]
+x, y = np.random.multivariate_normal(mean, cov, 10000).T
+
+# plt.hist2d(x, y, bins=30, cmap="Blues")
+plt.hexbin(x, y, gridsize=30, cmap="Oranges")
+cb = plt.colorbar()
+cb.set_label("counts in bin")
+plt.show()

python/ml-1.py

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+import tensorflow as tf
+import numpy as np
+
+print(tf.__version__)
+
+from tensorflow.contrib.learn.python.learn.datasets import base
+
+# Data files
+IRIS_TRAINING = "iris_training.csv"
+IRIS_TEST = "iris_test.csv"
+
+# Load datasets.
+training_set = base.load_csv_with_header(filename=IRIS_TRAINING,
+                                         features_dtype=np.float32,
+                                         target_dtype=np.int)
+test_set = base.load_csv_with_header(filename=IRIS_TEST,
+                                     features_dtype=np.float32,
+                                     target_dtype=np.int)
+
+# Specify that all features have real-value data
+feature_name = "flower_features"
+feature_columns = [tf.feature_column.numeric_column(feature_name,
+                                                    shape=[4])]
+classifier = tf.estimator.LinearClassifier(
+    feature_columns=feature_columns,
+    n_classes=3,
+    model_dir="/tmp/iris_model")
+
+def input_fn(dataset):
+    def _fn():
+        features = {feature_name: tf.constant(dataset.data)}
+        label = tf.constant(dataset.target)
+        return features, label
+    return _fn
+
+# Fit model.
+classifier.train(input_fn=input_fn(training_set),
+               steps=1000)
+print('fit done')
+
+# Evaluate accuracy.
+accuracy_score = classifier.evaluate(input_fn=input_fn(test_set),
+                                     steps=100)["accuracy"]
+print('\nAccuracy: {0:f}'.format(accuracy_score))
+
+# Export the model for serving
+feature_spec = {'flower_features': tf.FixedLenFeature(shape=[4], dtype=np.float32)}
+
+serving_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
+
+classifier.export_savedmodel(export_dir_base='/tmp/iris_model' + '/export',
+                            serving_input_receiver_fn=serving_fn)

python/np-demo.py

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+# Create 2 new lists height and weight
+import numpy as np
+height = [1.87,  1.87, 1.82, 1.91, 1.90, 1.85]
+weight = [81.65, 97.52, 95.25, 92.98, 86.18, 88.45]
+
+# Import the numpy package as np
+
+# Create 2 numpy arrays from height and weight
+
+np_height = np.array(height)
+np_weight = np.array(weight)
+print(type(np_height))
+# Calculate bmi
+bmi = np_weight / np_height ** 2
+
+# Print the result
+print(bmi)
+
+# For a boolean response
+# bmi > 23
+
+# Print only those observations above 23
+print(bmi[bmi > 25])

python/pandas-demo.py

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+import pandas as pd
+dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
+        "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
+        "area": [8.516, 17.10, 3.286, 9.597, 1.221],
+        "population": [200.4, 143.5, 1252, 1357, 52.98]}
+
+brics = pd.DataFrame(dict)
+print(brics)
+
+brics.index = ["BR", "RU", "IN", "CH", "SA"]
+print(brics)

python/plot-1.py

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+import numpy as np
+import matplotlib as matl
+import matplotlib.pyplot as plt
+import seaborn as sns
+from IPython.display import Image
+
+plt.style.use("seaborn-whitegrid")
+
+x = np.linspace(0, 10, 100)
+
+figure = plt.figure()
+ax = plt.axes()
+
+plt.plot(x, np.sin(x), "-", label="sin(x)")
+plt.plot(x, np.cos(x), "o", label="cos(x)")
+
+
+plt.legend()
+
+plt.show(block=True)

python/plot-2.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+rng = np.random.RandomState(0)
+
+
+for marker in ["o", ".", ",", "x", "+", "v", "^", "<", ">", "s", "d"]:
+    plt.plot(rng.rand(5), rng.rand(5), marker, label="marker='{0}'".format(marker))
+
+plt.legend(numpoints=1)
+
+plt.xlim(0, 2)
+
+plt.show()

python/random-color-spot-plot.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+rng = np.random.RandomState(0)
+x = rng.randn(100)
+y = rng.randn(100)
+colors = rng.rand(100)
+sizes = 1000 * rng.rand(100)
+
+plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap="viridis")
+plt.colorbar()  # show color scale
+
+plt.show()

python/servo-test.py

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+from adafruit_servokit import ServoKit
+kit = ServoKit(channels=16)
+
+print("hello starting")
+kit.servo[0].actuation_range = 160
+
+# angle can be  0 - 180
+kit.servo[0].angle = 180
+
+kit.servo[0].angle = 0
+kit.continuous_servo[0].throttle = 1

python/sub-plots.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+
+plt.style.use("seaborn-whitegrid")
+
+x = np.linspace(0, 10, 100)
+
+figure = plt.figure()
+ax = plt.axes()
+sub1 = plt.subplot(2, 2, 1)
+sub1.plot(x, np.sin(x), "-", label="sin(x)")
+sub1.legend()
+
+sub2 = plt.subplot(2, 2, 2)
+sub2 = plt.plot(x, np.cos(x), "o", label="cos(x)")
+sub2 = plt.legend()
+
+plt.subplot(2, 2, 3)
+plt.plot(x, np.tan(x), "-", label="tan(x)")
+plt.legend()
+
+
+plt.show(block=True)

python/us-stats-data.py

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+import pandas as pd
+import numpy as np
+
+populations = pd.read_csv("data/state-population.csv")
+areas = pd.read_csv("data/state-areas.csv")
+abbrevs = pd.read_csv("data/state-abbrevs.csv")
+
+merged = pd.merge(
+    populations, abbrevs, how="outer", left_on="state/region", right_on="abbreviation"
+)
+
+merged = merged.drop("abbreviation", 1)
+
+final = pd.merge(merged, areas, on="state", how="left")
+
+final.dropna(inplace=True)
+
+data2010 = final.query("year == 2010 & ages == 'total'")
+# print(data2010.head())
+
+data2010.set_index("state", inplace=True)
+density = data2010["population"] / data2010["area (sq. mi)"]
+density.sort_values(ascending=False, inplace=True)
+print(density.tail())