From 078982d3aac0be5190d45d438da5da291da9984c Mon Sep 17 00:00:00 2001 From: Junv Zhao Date: Sat, 22 Jun 2019 22:37:24 +1000 Subject: [PATCH] add home macbook code snippets --- .gitignore | 2 + birth-rate.py | 16 + data/births.csv | 15548 ++++++++++++++++++++++++++++++++++++ data/state-abbrevs.csv | 52 + data/state-areas.csv | 53 + data/state-population.csv | 2545 ++++++ ds-026.py | 17 + hello.py | 27 + iris_test.csv | 31 + iris_training.csv | 121 + ml-1.py | 52 + np-demo.py | 23 + pandas-demo.py | 11 + servo-test.py | 11 + us-stats-data.py | 24 + 15 files changed, 18533 insertions(+) create mode 100644 .gitignore create mode 100644 birth-rate.py create mode 100644 data/births.csv create mode 100644 data/state-abbrevs.csv create mode 100644 data/state-areas.csv create mode 100644 data/state-population.csv create mode 100644 ds-026.py create mode 100644 hello.py create mode 100644 iris_test.csv create mode 100644 iris_training.csv create mode 100644 ml-1.py create mode 100644 np-demo.py create mode 100644 pandas-demo.py create mode 100644 servo-test.py create mode 100644 us-stats-data.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..98f27bf --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.vscode +.mypy_cache diff --git a/birth-rate.py b/birth-rate.py new file mode 100644 index 0000000..32e2f90 --- /dev/null +++ b/birth-rate.py @@ -0,0 +1,16 @@ +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) diff --git a/data/births.csv b/data/births.csv new file mode 100644 index 0000000..4a5bb7a --- /dev/null +++ b/data/births.csv @@ -0,0 +1,15548 @@ +year,month,day,gender,births +1969,1,1,F,4046 +1969,1,1,M,4440 +1969,1,2,F,4454 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-0,0 +1,52 @@ +"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" \ No newline at end of file diff --git a/data/state-areas.csv b/data/state-areas.csv new file mode 100644 index 0000000..322345c --- /dev/null +++ b/data/state-areas.csv @@ -0,0 +1,53 @@ +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 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b/hello.py @@ -0,0 +1,27 @@ +# 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) diff --git a/iris_test.csv b/iris_test.csv new file mode 100644 index 0000000..5929d91 --- /dev/null +++ b/iris_test.csv @@ -0,0 +1,31 @@ +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 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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) diff --git a/np-demo.py b/np-demo.py new file mode 100644 index 0000000..145e121 --- /dev/null +++ b/np-demo.py @@ -0,0 +1,23 @@ +# 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]) diff --git a/pandas-demo.py b/pandas-demo.py new file mode 100644 index 0000000..1b792b5 --- /dev/null +++ b/pandas-demo.py @@ -0,0 +1,11 @@ +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) diff --git a/servo-test.py b/servo-test.py new file mode 100644 index 0000000..2c89375 --- /dev/null +++ b/servo-test.py @@ -0,0 +1,11 @@ +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 diff --git a/us-stats-data.py b/us-stats-data.py new file mode 100644 index 0000000..d778628 --- /dev/null +++ b/us-stats-data.py @@ -0,0 +1,24 @@ +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())