add home macbook code snippets

.gitignore

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+.vscode
+.mypy_cache

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)

data/state-abbrevs.csv

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+"state","abbreviation"
+"Alabama","AL"
+"Alaska","AK"
+"Arizona","AZ"
+"Arkansas","AR"
+"California","CA"
+"Colorado","CO"
+"Connecticut","CT"
+"Delaware","DE"
+"District of Columbia","DC"
+"Florida","FL"
+"Georgia","GA"
+"Hawaii","HI"
+"Idaho","ID"
+"Illinois","IL"
+"Indiana","IN"
+"Iowa","IA"
+"Kansas","KS"
+"Kentucky","KY"
+"Louisiana","LA"
+"Maine","ME"
+"Montana","MT"
+"Nebraska","NE"
+"Nevada","NV"
+"New Hampshire","NH"
+"New Jersey","NJ"
+"New Mexico","NM"
+"New York","NY"
+"North Carolina","NC"
+"North Dakota","ND"
+"Ohio","OH"
+"Oklahoma","OK"
+"Oregon","OR"
+"Maryland","MD"
+"Massachusetts","MA"
+"Michigan","MI"
+"Minnesota","MN"
+"Mississippi","MS"
+"Missouri","MO"
+"Pennsylvania","PA"
+"Rhode Island","RI"
+"South Carolina","SC"
+"South Dakota","SD"
+"Tennessee","TN"
+"Texas","TX"
+"Utah","UT"
+"Vermont","VT"
+"Virginia","VA"
+"Washington","WA"
+"West Virginia","WV"
+"Wisconsin","WI"
+"Wyoming","WY"

data/state-areas.csv

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+state,area (sq. mi)
+Alabama,52423
+Alaska,656425
+Arizona,114006
+Arkansas,53182
+California,163707
+Colorado,104100
+Connecticut,5544
+Delaware,1954
+Florida,65758
+Georgia,59441
+Hawaii,10932
+Idaho,83574
+Illinois,57918
+Indiana,36420
+Iowa,56276
+Kansas,82282
+Kentucky,40411
+Louisiana,51843
+Maine,35387
+Maryland,12407
+Massachusetts,10555
+Michigan,96810
+Minnesota,86943
+Mississippi,48434
+Missouri,69709
+Montana,147046
+Nebraska,77358
+Nevada,110567
+New Hampshire,9351
+New Jersey,8722
+New Mexico,121593
+New York,54475
+North Carolina,53821
+North Dakota,70704
+Ohio,44828
+Oklahoma,69903
+Oregon,98386
+Pennsylvania,46058
+Rhode Island,1545
+South Carolina,32007
+South Dakota,77121
+Tennessee,42146
+Texas,268601
+Utah,84904
+Vermont,9615
+Virginia,42769
+Washington,71303
+West Virginia,24231
+Wisconsin,65503
+Wyoming,97818
+District of Columbia,68
+Puerto Rico,3515

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)

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)

iris_test.csv

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+30,4,setosa,versicolor,virginica
+5.9,3.0,4.2,1.5,1
+6.9,3.1,5.4,2.1,2
+5.1,3.3,1.7,0.5,0
+6.0,3.4,4.5,1.6,1
+5.5,2.5,4.0,1.3,1
+6.2,2.9,4.3,1.3,1
+5.5,4.2,1.4,0.2,0
+6.3,2.8,5.1,1.5,2
+5.6,3.0,4.1,1.3,1
+6.7,2.5,5.8,1.8,2
+7.1,3.0,5.9,2.1,2
+4.3,3.0,1.1,0.1,0
+5.6,2.8,4.9,2.0,2
+5.5,2.3,4.0,1.3,1
+6.0,2.2,4.0,1.0,1
+5.1,3.5,1.4,0.2,0
+5.7,2.6,3.5,1.0,1
+4.8,3.4,1.9,0.2,0
+5.1,3.4,1.5,0.2,0
+5.7,2.5,5.0,2.0,2
+5.4,3.4,1.7,0.2,0
+5.6,3.0,4.5,1.5,1
+6.3,2.9,5.6,1.8,2
+6.3,2.5,4.9,1.5,1
+5.8,2.7,3.9,1.2,1
+6.1,3.0,4.6,1.4,1
+5.2,4.1,1.5,0.1,0
+6.7,3.1,4.7,1.5,1
+6.7,3.3,5.7,2.5,2
+6.4,2.9,4.3,1.3,1

iris_training.csv

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+120,4,setosa,versicolor,virginica
+6.4,2.8,5.6,2.2,2
+5.0,2.3,3.3,1.0,1
+4.9,2.5,4.5,1.7,2
+4.9,3.1,1.5,0.1,0
+5.7,3.8,1.7,0.3,0
+4.4,3.2,1.3,0.2,0
+5.4,3.4,1.5,0.4,0
+6.9,3.1,5.1,2.3,2
+6.7,3.1,4.4,1.4,1
+5.1,3.7,1.5,0.4,0
+5.2,2.7,3.9,1.4,1
+6.9,3.1,4.9,1.5,1
+5.8,4.0,1.2,0.2,0
+5.4,3.9,1.7,0.4,0
+7.7,3.8,6.7,2.2,2
+6.3,3.3,4.7,1.6,1
+6.8,3.2,5.9,2.3,2
+7.6,3.0,6.6,2.1,2
+6.4,3.2,5.3,2.3,2
+5.7,4.4,1.5,0.4,0
+6.7,3.3,5.7,2.1,2
+6.4,2.8,5.6,2.1,2
+5.4,3.9,1.3,0.4,0
+6.1,2.6,5.6,1.4,2
+7.2,3.0,5.8,1.6,2
+5.2,3.5,1.5,0.2,0
+5.8,2.6,4.0,1.2,1
+5.9,3.0,5.1,1.8,2
+5.4,3.0,4.5,1.5,1
+6.7,3.0,5.0,1.7,1
+6.3,2.3,4.4,1.3,1
+5.1,2.5,3.0,1.1,1
+6.4,3.2,4.5,1.5,1
+6.8,3.0,5.5,2.1,2
+6.2,2.8,4.8,1.8,2
+6.9,3.2,5.7,2.3,2
+6.5,3.2,5.1,2.0,2
+5.8,2.8,5.1,2.4,2
+5.1,3.8,1.5,0.3,0
+4.8,3.0,1.4,0.3,0
+7.9,3.8,6.4,2.0,2
+5.8,2.7,5.1,1.9,2
+6.7,3.0,5.2,2.3,2
+5.1,3.8,1.9,0.4,0
+4.7,3.2,1.6,0.2,0
+6.0,2.2,5.0,1.5,2
+4.8,3.4,1.6,0.2,0
+7.7,2.6,6.9,2.3,2
+4.6,3.6,1.0,0.2,0
+7.2,3.2,6.0,1.8,2
+5.0,3.3,1.4,0.2,0
+6.6,3.0,4.4,1.4,1
+6.1,2.8,4.0,1.3,1
+5.0,3.2,1.2,0.2,0
+7.0,3.2,4.7,1.4,1
+6.0,3.0,4.8,1.8,2
+7.4,2.8,6.1,1.9,2
+5.8,2.7,5.1,1.9,2
+6.2,3.4,5.4,2.3,2
+5.0,2.0,3.5,1.0,1
+5.6,2.5,3.9,1.1,1
+6.7,3.1,5.6,2.4,2
+6.3,2.5,5.0,1.9,2
+6.4,3.1,5.5,1.8,2
+6.2,2.2,4.5,1.5,1
+7.3,2.9,6.3,1.8,2
+4.4,3.0,1.3,0.2,0
+7.2,3.6,6.1,2.5,2
+6.5,3.0,5.5,1.8,2
+5.0,3.4,1.5,0.2,0
+4.7,3.2,1.3,0.2,0
+6.6,2.9,4.6,1.3,1
+5.5,3.5,1.3,0.2,0
+7.7,3.0,6.1,2.3,2
+6.1,3.0,4.9,1.8,2
+4.9,3.1,1.5,0.1,0
+5.5,2.4,3.8,1.1,1
+5.7,2.9,4.2,1.3,1
+6.0,2.9,4.5,1.5,1
+6.4,2.7,5.3,1.9,2
+5.4,3.7,1.5,0.2,0
+6.1,2.9,4.7,1.4,1
+6.5,2.8,4.6,1.5,1
+5.6,2.7,4.2,1.3,1
+6.3,3.4,5.6,2.4,2
+4.9,3.1,1.5,0.1,0
+6.8,2.8,4.8,1.4,1
+5.7,2.8,4.5,1.3,1
+6.0,2.7,5.1,1.6,1
+5.0,3.5,1.3,0.3,0
+6.5,3.0,5.2,2.0,2
+6.1,2.8,4.7,1.2,1
+5.1,3.5,1.4,0.3,0
+4.6,3.1,1.5,0.2,0
+6.5,3.0,5.8,2.2,2
+4.6,3.4,1.4,0.3,0
+4.6,3.2,1.4,0.2,0
+7.7,2.8,6.7,2.0,2
+5.9,3.2,4.8,1.8,1
+5.1,3.8,1.6,0.2,0
+4.9,3.0,1.4,0.2,0
+4.9,2.4,3.3,1.0,1
+4.5,2.3,1.3,0.3,0
+5.8,2.7,4.1,1.0,1
+5.0,3.4,1.6,0.4,0
+5.2,3.4,1.4,0.2,0
+5.3,3.7,1.5,0.2,0
+5.0,3.6,1.4,0.2,0
+5.6,2.9,3.6,1.3,1
+4.8,3.1,1.6,0.2,0
+6.3,2.7,4.9,1.8,2
+5.7,2.8,4.1,1.3,1
+5.0,3.0,1.6,0.2,0
+6.3,3.3,6.0,2.5,2
+5.0,3.5,1.6,0.6,0
+5.5,2.6,4.4,1.2,1
+5.7,3.0,4.2,1.2,1
+4.4,2.9,1.4,0.2,0
+4.8,3.0,1.4,0.1,0
+5.5,2.4,3.7,1.0,1

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)

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])

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)

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

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())