Auto commit

data/highs_lows.py

-import csv
-
-filename = 'sitka_weather_07-2014.csv'
-with open(filename) as f:
-    reader = csv.reader(f)
-    header_row = next(reader)
-    print(header_row)
\ No newline at end of file

matplotlib_dataScience/Ipynb_importer.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import io, os,sys,types
+from IPython import get_ipython
+from nbformat import read
+from IPython.core.interactiveshell import InteractiveShell
+
+class NotebookFinder(object):
+    """Module finder that locates Jupyter Notebooks"""
+    def __init__(self):
+        self.loaders = {}
+
+    def find_module(self, fullname, path=None):
+        nb_path = find_notebook(fullname, path)
+        if not nb_path:
+            return
+
+        key = path
+        if path:
+            # lists aren't hashable
+            key = os.path.sep.join(path)
+
+        if key not in self.loaders:
+            self.loaders[key] = NotebookLoader(path)
+        return self.loaders[key]
+
+def find_notebook(fullname, path=None):
+    """find a notebook, given its fully qualified name and an optional path
+
+    This turns "foo.bar" into "foo/bar.ipynb"
+    and tries turning "Foo_Bar" into "Foo Bar" if Foo_Bar
+    does not exist.
+    """
+    name = fullname.rsplit('.', 1)[-1]
+    if not path:
+        path = ['']
+    for d in path:
+        nb_path = os.path.join(d, name + ".ipynb")
+        if os.path.isfile(nb_path):
+            return nb_path
+        # let import Notebook_Name find "Notebook Name.ipynb"
+        nb_path = nb_path.replace("_", " ")
+        if os.path.isfile(nb_path):
+            return nb_path
+
+class NotebookLoader(object):
+    """Module Loader for Jupyter Notebooks"""
+    def __init__(self, path=None):
+        self.shell = InteractiveShell.instance()
+        self.path = path
+
+    def load_module(self, fullname):
+        """import a notebook as a module"""
+        path = find_notebook(fullname, self.path)
+
+        print ("importing Jupyter notebook from %s" % path)
+
+        # load the notebook object
+        with io.open(path, 'r', encoding='utf-8') as f:
+            nb = read(f, 4)
+
+
+        # create the module and add it to sys.modules
+        # if name in sys.modules:
+        #    return sys.modules[name]
+        mod = types.ModuleType(fullname)
+        mod.__file__ = path
+        mod.__loader__ = self
+        mod.__dict__['get_ipython'] = get_ipython
+        sys.modules[fullname] = mod
+
+        # extra work to ensure that magics that would affect the user_ns
+        # actually affect the notebook module's ns
+        save_user_ns = self.shell.user_ns
+        self.shell.user_ns = mod.__dict__
+
+        try:
+          for cell in nb.cells:
+            if cell.cell_type == 'code':
+                # transform the input to executable Python
+                code = self.shell.input_transformer_manager.transform_cell(cell.source)
+                # run the code in themodule
+                exec(code, mod.__dict__)
+        finally:
+            self.shell.user_ns = save_user_ns
+        return mod
+sys.meta_path.append(NotebookFinder())
+

matplotlib_dataScience/btc_close_2017.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1a6da5bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2017-01-01 is month 1 week 52, Sunday ,the close price is 6928 RMB\n",
+      "2017-01-02 is month 1 week 1, Monday ,the close price is 7070 RMB\n",
+      "2017-01-03 is month 1 week 1, Tuesday ,the close price is 7175 RMB\n",
+      "2017-01-04 is month 1 week 1, Wednesday ,the close price is 7835 RMB\n",
+      "2017-01-05 is month 1 week 1, Thursday ,the close price is 6928 RMB\n",
+      "2017-01-06 is month 1 week 1, Friday ,the close price is 6196 RMB\n",
+      "2017-01-07 is month 1 week 1, Saturday ,the close price is 6262 RMB\n",
+      "2017-01-08 is month 1 week 1, Sunday ,the close price is 6319 RMB\n",
+      "2017-01-09 is month 1 week 2, Monday ,the close price is 6239 RMB\n",
+      "2017-01-10 is month 1 week 2, Tuesday ,the close price is 6263 RMB\n",
+      "2017-01-11 is month 1 week 2, Wednesday ,the close price is 5383 RMB\n",
+      "2017-01-12 is month 1 week 2, Thursday ,the close price is 5566 RMB\n",
+      "2017-01-13 is month 1 week 2, Friday ,the close price is 5700 RMB\n",
+      "2017-01-14 is month 1 week 2, Saturday ,the close price is 5648 RMB\n",
+      "2017-01-15 is month 1 week 2, Sunday ,the close price is 5674 RMB\n",
+      "2017-01-16 is month 1 week 3, Monday ,the close price is 5730 RMB\n",
+      "2017-01-17 is month 1 week 3, Tuesday ,the close price is 6202 RMB\n",
+      "2017-01-18 is month 1 week 3, Wednesday ,the close price is 6047 RMB\n",
+      "2017-01-19 is month 1 week 3, Thursday ,the close price is 6170 RMB\n",
+      "2017-01-20 is month 1 week 3, Friday ,the close price is 6131 RMB\n",
+      "2017-01-21 is month 1 week 3, Saturday ,the close price is 6326 RMB\n",
+      "2017-01-22 is month 1 week 3, Sunday ,the close price is 6362 RMB\n",
+      "2017-01-23 is month 1 week 4, Monday ,the close price is 6255 RMB\n",
+      "2017-01-24 is month 1 week 4, Tuesday ,the close price is 6074 RMB\n",
+      "2017-01-25 is month 1 week 4, Wednesday ,the close price is 6154 RMB\n",
+      "2017-01-26 is month 1 week 4, Thursday ,the close price is 6295 RMB\n",
+      "2017-01-27 is month 1 week 4, Friday ,the close price is 6320 RMB\n",
+      "2017-01-28 is month 1 week 4, Saturday ,the close price is 6332 RMB\n",
+      "2017-01-29 is month 1 week 4, Sunday ,the close price is 6289 RMB\n",
+      "2017-01-30 is month 1 week 5, Monday ,the close price is 6332 RMB\n",
+      "2017-01-31 is month 1 week 5, Tuesday ,the close price is 6657 RMB\n",
+      "2017-02-01 is month 2 week 5, Wednesday ,the close price is 6793 RMB\n",
+      "2017-02-02 is month 2 week 5, Thursday ,the close price is 6934 RMB\n",
+      "2017-02-03 is month 2 week 5, Friday ,the close price is 6995 RMB\n",
+      "2017-02-04 is month 2 week 5, Saturday ,the close price is 7102 RMB\n",
+      "2017-02-05 is month 2 week 5, Sunday ,the close price is 6965 RMB\n",
+      "2017-02-06 is month 2 week 6, Monday ,the close price is 7034 RMB\n",
+      "2017-02-07 is month 2 week 6, Tuesday ,the close price is 7245 RMB\n",
+      "2017-02-08 is month 2 week 6, Wednesday ,the close price is 7246 RMB\n",
+      "2017-02-09 is month 2 week 6, Thursday ,the close price is 6811 RMB\n",
+      "2017-02-10 is month 2 week 6, Friday ,the close price is 6833 RMB\n",
+      "2017-02-11 is month 2 week 6, Saturday ,the close price is 6946 RMB\n",
+      "2017-02-12 is month 2 week 6, Sunday ,the close price is 6883 RMB\n",
+      "2017-02-13 is month 2 week 7, Monday ,the close price is 6858 RMB\n",
+      "2017-02-14 is month 2 week 7, Tuesday ,the close price is 6930 RMB\n",
+      "2017-02-15 is month 2 week 7, Wednesday ,the close price is 6935 RMB\n",
+      "2017-02-16 is month 2 week 7, Thursday ,the close price is 7088 RMB\n",
+      "2017-02-17 is month 2 week 7, Friday ,the close price is 7229 RMB\n",
+      "2017-02-18 is month 2 week 7, Saturday ,the close price is 7267 RMB\n",
+      "2017-02-19 is month 2 week 7, Sunday ,the close price is 7220 RMB\n",
+      "2017-02-20 is month 2 week 8, Monday ,the close price is 7450 RMB\n",
+      "2017-02-21 is month 2 week 8, Tuesday ,the close price is 7732 RMB\n",
+      "2017-02-22 is month 2 week 8, Wednesday ,the close price is 7716 RMB\n",
+      "2017-02-23 is month 2 week 8, Thursday ,the close price is 8092 RMB\n",
+      "2017-02-24 is month 2 week 8, Friday ,the close price is 8109 RMB\n",
+      "2017-02-25 is month 2 week 8, Saturday ,the close price is 7908 RMB\n",
+      "2017-02-26 is month 2 week 8, Sunday ,the close price is 8137 RMB\n",
+      "2017-02-27 is month 2 week 9, Monday ,the close price is 8206 RMB\n",
+      "2017-02-28 is month 2 week 9, Tuesday ,the close price is 8176 RMB\n",
+      "2017-03-01 is month 3 week 9, Wednesday ,the close price is 8464 RMB\n",
+      "2017-03-02 is month 3 week 9, Thursday ,the close price is 8688 RMB\n",
+      "2017-03-03 is month 3 week 9, Friday ,the close price is 8900 RMB\n",
+      "2017-03-04 is month 3 week 9, Saturday ,the close price is 8741 RMB\n",
+      "2017-03-05 is month 3 week 9, Sunday ,the close price is 8816 RMB\n",
+      "2017-03-06 is month 3 week 10, Monday ,the close price is 8832 RMB\n",
+      "2017-03-07 is month 3 week 10, Tuesday ,the close price is 8504 RMB\n",
+      "2017-03-08 is month 3 week 10, Wednesday ,the close price is 7953 RMB\n",
+      "2017-03-09 is month 3 week 10, Thursday ,the close price is 8235 RMB\n",
+      "2017-03-10 is month 3 week 10, Friday ,the close price is 7716 RMB\n",
+      "2017-03-11 is month 3 week 10, Saturday ,the close price is 8161 RMB\n",
+      "2017-03-12 is month 3 week 10, Sunday ,the close price is 8441 RMB\n",
+      "2017-03-13 is month 3 week 11, Monday ,the close price is 8595 RMB\n",
+      "2017-03-14 is month 3 week 11, Tuesday ,the close price is 8616 RMB\n",
+      "2017-03-15 is month 3 week 11, Wednesday ,the close price is 8711 RMB\n",
+      "2017-03-16 is month 3 week 11, Thursday ,the close price is 8091 RMB\n",
+      "2017-03-17 is month 3 week 11, Friday ,the close price is 7379 RMB\n",
+      "2017-03-18 is month 3 week 11, Saturday ,the close price is 6694 RMB\n",
+      "2017-03-19 is month 3 week 11, Sunday ,the close price is 7028 RMB\n",
+      "2017-03-20 is month 3 week 12, Monday ,the close price is 7196 RMB\n",
+      "2017-03-21 is month 3 week 12, Tuesday ,the close price is 7680 RMB\n",
+      "2017-03-22 is month 3 week 12, Wednesday ,the close price is 7139 RMB\n",
+      "2017-03-23 is month 3 week 12, Thursday ,the close price is 7092 RMB\n",
+      "2017-03-24 is month 3 week 12, Friday ,the close price is 6437 RMB\n",
+      "2017-03-25 is month 3 week 12, Saturday ,the close price is 6640 RMB\n",
+      "2017-03-26 is month 3 week 12, Sunday ,the close price is 6623 RMB\n",
+      "2017-03-27 is month 3 week 13, Monday ,the close price is 7151 RMB\n",
+      "2017-03-28 is month 3 week 13, Tuesday ,the close price is 7184 RMB\n",
+      "2017-03-29 is month 3 week 13, Wednesday ,the close price is 7168 RMB\n",
+      "2017-03-30 is month 3 week 13, Thursday ,the close price is 7146 RMB\n",
+      "2017-03-31 is month 3 week 13, Friday ,the close price is 7439 RMB\n",
+      "2017-04-01 is month 4 week 13, Saturday ,the close price is 7506 RMB\n",
+      "2017-04-02 is month 4 week 13, Sunday ,the close price is 7566 RMB\n",
+      "2017-04-03 is month 4 week 14, Monday ,the close price is 7903 RMB\n",
+      "2017-04-04 is month 4 week 14, Tuesday ,the close price is 7874 RMB\n",
+      "2017-04-05 is month 4 week 14, Wednesday ,the close price is 7827 RMB\n",
+      "2017-04-06 is month 4 week 14, Thursday ,the close price is 8212 RMB\n",
+      "2017-04-07 is month 4 week 14, Friday ,the close price is 8236 RMB\n",
+      "2017-04-08 is month 4 week 14, Saturday ,the close price is 8180 RMB\n",
+      "2017-04-09 is month 4 week 14, Sunday ,the close price is 8354 RMB\n",
+      "2017-04-10 is month 4 week 15, Monday ,the close price is 8375 RMB\n",
+      "2017-04-11 is month 4 week 15, Tuesday ,the close price is 8442 RMB\n",
+      "2017-04-12 is month 4 week 15, Wednesday ,the close price is 8382 RMB\n",
+      "2017-04-13 is month 4 week 15, Thursday ,the close price is 8117 RMB\n",
+      "2017-04-14 is month 4 week 15, Friday ,the close price is 8151 RMB\n",
+      "2017-04-15 is month 4 week 15, Saturday ,the close price is 8129 RMB\n",
+      "2017-04-16 is month 4 week 15, Sunday ,the close price is 8167 RMB\n",
+      "2017-04-17 is month 4 week 16, Monday ,the close price is 8267 RMB\n",
+      "2017-04-18 is month 4 week 16, Tuesday ,the close price is 8379 RMB\n",
+      "2017-04-19 is month 4 week 16, Wednesday ,the close price is 8436 RMB\n",
+      "2017-04-20 is month 4 week 16, Thursday ,the close price is 8639 RMB\n",
+      "2017-04-21 is month 4 week 16, Friday ,the close price is 8654 RMB\n",
+      "2017-04-22 is month 4 week 16, Saturday ,the close price is 8567 RMB\n",
+      "2017-04-23 is month 4 week 16, Sunday ,the close price is 8458 RMB\n",
+      "2017-04-24 is month 4 week 17, Monday ,the close price is 8594 RMB\n",
+      "2017-04-25 is month 4 week 17, Tuesday ,the close price is 8700 RMB\n",
+      "2017-04-26 is month 4 week 17, Wednesday ,the close price is 8857 RMB\n",
+      "2017-04-27 is month 4 week 17, Thursday ,the close price is 9167 RMB\n",
+      "2017-04-28 is month 4 week 17, Friday ,the close price is 9101 RMB\n",
+      "2017-04-29 is month 4 week 17, Saturday ,the close price is 9149 RMB\n",
+      "2017-04-30 is month 4 week 17, Sunday ,the close price is 9325 RMB\n",
+      "2017-05-01 is month 5 week 18, Monday ,the close price is 9665 RMB\n",
+      "2017-05-02 is month 5 week 18, Tuesday ,the close price is 9944 RMB\n",
+      "2017-05-03 is month 5 week 18, Wednesday ,the close price is 10292 RMB\n",
+      "2017-05-04 is month 5 week 18, Thursday ,the close price is 10452 RMB\n",
+      "2017-05-05 is month 5 week 18, Friday ,the close price is 10439 RMB\n",
+      "2017-05-06 is month 5 week 18, Saturday ,the close price is 10688 RMB\n",
+      "2017-05-07 is month 5 week 18, Sunday ,the close price is 10660 RMB\n",
+      "2017-05-08 is month 5 week 19, Monday ,the close price is 11317 RMB\n",
+      "2017-05-09 is month 5 week 19, Tuesday ,the close price is 11794 RMB\n",
+      "2017-05-10 is month 5 week 19, Wednesday ,the close price is 12126 RMB\n",
+      "2017-05-11 is month 5 week 19, Thursday ,the close price is 12478 RMB\n",
+      "2017-05-12 is month 5 week 19, Friday ,the close price is 11569 RMB\n",
+      "2017-05-13 is month 5 week 19, Saturday ,the close price is 12141 RMB\n",
+      "2017-05-14 is month 5 week 19, Sunday ,the close price is 12229 RMB\n",
+      "2017-05-15 is month 5 week 20, Monday ,the close price is 11701 RMB\n",
+      "2017-05-16 is month 5 week 20, Tuesday ,the close price is 11835 RMB\n",
+      "2017-05-17 is month 5 week 20, Wednesday ,the close price is 12403 RMB\n",
+      "2017-05-18 is month 5 week 20, Thursday ,the close price is 13002 RMB\n",
+      "2017-05-19 is month 5 week 20, Friday ,the close price is 13549 RMB\n",
+      "2017-05-20 is month 5 week 20, Saturday ,the close price is 14127 RMB\n",
+      "2017-05-21 is month 5 week 20, Sunday ,the close price is 14091 RMB\n",
+      "2017-05-22 is month 5 week 21, Monday ,the close price is 14731 RMB\n",
+      "2017-05-23 is month 5 week 21, Tuesday ,the close price is 15784 RMB\n",
+      "2017-05-24 is month 5 week 21, Wednesday ,the close price is 17061 RMB\n",
+      "2017-05-25 is month 5 week 21, Thursday ,the close price is 16190 RMB\n",
+      "2017-05-26 is month 5 week 21, Friday ,the close price is 15402 RMB\n",
+      "2017-05-27 is month 5 week 21, Saturday ,the close price is 14440 RMB\n",
+      "2017-05-28 is month 5 week 21, Sunday ,the close price is 15139 RMB\n",
+      "2017-05-29 is month 5 week 22, Monday ,the close price is 15700 RMB\n",
+      "2017-05-30 is month 5 week 22, Tuesday ,the close price is 15064 RMB\n",
+      "2017-05-31 is month 5 week 22, Wednesday ,the close price is 15869 RMB\n",
+      "2017-06-01 is month 6 week 22, Thursday ,the close price is 16693 RMB\n",
+      "2017-06-02 is month 6 week 22, Friday ,the close price is 17149 RMB\n",
+      "2017-06-03 is month 6 week 22, Saturday ,the close price is 17410 RMB\n",
+      "2017-06-04 is month 6 week 22, Sunday ,the close price is 17399 RMB\n",
+      "2017-06-05 is month 6 week 23, Monday ,the close price is 18621 RMB\n",
+      "2017-06-06 is month 6 week 23, Tuesday ,the close price is 19797 RMB\n",
+      "2017-06-07 is month 6 week 23, Wednesday ,the close price is 18205 RMB\n",
+      "2017-06-08 is month 6 week 23, Thursday ,the close price is 19209 RMB\n",
+      "2017-06-09 is month 6 week 23, Friday ,the close price is 19218 RMB\n",
+      "2017-06-10 is month 6 week 23, Saturday ,the close price is 20004 RMB\n",
+      "2017-06-11 is month 6 week 23, Sunday ,the close price is 20472 RMB\n",
+      "2017-06-12 is month 6 week 24, Monday ,the close price is 18234 RMB\n",
+      "2017-06-13 is month 6 week 24, Tuesday ,the close price is 18615 RMB\n",
+      "2017-06-14 is month 6 week 24, Wednesday ,the close price is 16946 RMB\n",
+      "2017-06-15 is month 6 week 24, Thursday ,the close price is 16724 RMB\n",
+      "2017-06-16 is month 6 week 24, Friday ,the close price is 17217 RMB\n",
+      "2017-06-17 is month 6 week 24, Saturday ,the close price is 18142 RMB\n",
+      "2017-06-18 is month 6 week 24, Sunday ,the close price is 17535 RMB\n",
+      "2017-06-19 is month 6 week 25, Monday ,the close price is 18015 RMB\n",
+      "2017-06-20 is month 6 week 25, Tuesday ,the close price is 18975 RMB\n",
+      "2017-06-21 is month 6 week 25, Wednesday ,the close price is 18522 RMB\n",
+      "2017-06-22 is month 6 week 25, Thursday ,the close price is 18733 RMB\n",
+      "2017-06-23 is month 6 week 25, Friday ,the close price is 18720 RMB\n",
+      "2017-06-24 is month 6 week 25, Saturday ,the close price is 17906 RMB\n",
+      "2017-06-25 is month 6 week 25, Sunday ,the close price is 17734 RMB\n",
+      "2017-06-26 is month 6 week 26, Monday ,the close price is 17001 RMB\n",
+      "2017-06-27 is month 6 week 26, Tuesday ,the close price is 17666 RMB\n",
+      "2017-06-28 is month 6 week 26, Wednesday ,the close price is 17575 RMB\n",
+      "2017-06-29 is month 6 week 26, Thursday ,the close price is 17385 RMB\n",
+      "2017-06-30 is month 6 week 26, Friday ,the close price is 16943 RMB\n",
+      "2017-07-01 is month 7 week 26, Saturday ,the close price is 16674 RMB\n",
+      "2017-07-02 is month 7 week 26, Sunday ,the close price is 17150 RMB\n",
+      "2017-07-03 is month 7 week 27, Monday ,the close price is 17549 RMB\n",
+      "2017-07-04 is month 7 week 27, Tuesday ,the close price is 17851 RMB\n",
+      "2017-07-05 is month 7 week 27, Wednesday ,the close price is 17812 RMB\n",
+      "2017-07-06 is month 7 week 27, Thursday ,the close price is 17813 RMB\n",
+      "2017-07-07 is month 7 week 27, Friday ,the close price is 17156 RMB\n",
+      "2017-07-08 is month 7 week 27, Saturday ,the close price is 17557 RMB\n",
+      "2017-07-09 is month 7 week 27, Sunday ,the close price is 17189 RMB\n",
+      "2017-07-10 is month 7 week 28, Monday ,the close price is 16137 RMB\n",
+      "2017-07-11 is month 7 week 28, Tuesday ,the close price is 15865 RMB\n",
+      "2017-07-12 is month 7 week 28, Wednesday ,the close price is 16446 RMB\n",
+      "2017-07-13 is month 7 week 28, Thursday ,the close price is 16036 RMB\n",
+      "2017-07-14 is month 7 week 28, Friday ,the close price is 15132 RMB\n",
+      "2017-07-15 is month 7 week 28, Saturday ,the close price is 13510 RMB\n",
+      "2017-07-16 is month 7 week 28, Sunday ,the close price is 13075 RMB\n",
+      "2017-07-17 is month 7 week 29, Monday ,the close price is 15192 RMB\n",
+      "2017-07-18 is month 7 week 29, Tuesday ,the close price is 15706 RMB\n",
+      "2017-07-19 is month 7 week 29, Wednesday ,the close price is 15491 RMB\n",
+      "2017-07-20 is month 7 week 29, Thursday ,the close price is 19449 RMB\n",
+      "2017-07-21 is month 7 week 29, Friday ,the close price is 18231 RMB\n",
+      "2017-07-22 is month 7 week 29, Saturday ,the close price is 19210 RMB\n",
+      "2017-07-23 is month 7 week 29, Sunday ,the close price is 18585 RMB\n",
+      "2017-07-24 is month 7 week 30, Monday ,the close price is 18762 RMB\n",
+      "2017-07-25 is month 7 week 30, Tuesday ,the close price is 17489 RMB\n",
+      "2017-07-26 is month 7 week 30, Wednesday ,the close price is 17219 RMB\n",
+      "2017-07-27 is month 7 week 30, Thursday ,the close price is 18188 RMB\n",
+      "2017-07-28 is month 7 week 30, Friday ,the close price is 18898 RMB\n",
+      "2017-07-29 is month 7 week 30, Saturday ,the close price is 18326 RMB\n",
+      "2017-07-30 is month 7 week 30, Sunday ,the close price is 18499 RMB\n",
+      "2017-07-31 is month 7 week 31, Monday ,the close price is 19334 RMB\n",
+      "2017-08-01 is month 8 week 31, Tuesday ,the close price is 18376 RMB\n",
+      "2017-08-02 is month 8 week 31, Wednesday ,the close price is 18305 RMB\n",
+      "2017-08-03 is month 8 week 31, Thursday ,the close price is 18901 RMB\n",
+      "2017-08-04 is month 8 week 31, Friday ,the close price is 19412 RMB\n",
+      "2017-08-05 is month 8 week 31, Saturday ,the close price is 22227 RMB\n",
+      "2017-08-06 is month 8 week 31, Sunday ,the close price is 22027 RMB\n",
+      "2017-08-07 is month 8 week 32, Monday ,the close price is 23063 RMB\n",
+      "2017-08-08 is month 8 week 32, Tuesday ,the close price is 23332 RMB\n",
+      "2017-08-09 is month 8 week 32, Wednesday ,the close price is 22541 RMB\n",
+      "2017-08-10 is month 8 week 32, Thursday ,the close price is 22904 RMB\n",
+      "2017-08-11 is month 8 week 32, Friday ,the close price is 24526 RMB\n",
+      "2017-08-12 is month 8 week 32, Saturday ,the close price is 26109 RMB\n",
+      "2017-08-13 is month 8 week 32, Sunday ,the close price is 27390 RMB\n",
+      "2017-08-14 is month 8 week 33, Monday ,the close price is 29237 RMB\n",
+      "2017-08-15 is month 8 week 33, Tuesday ,the close price is 28073 RMB\n",
+      "2017-08-16 is month 8 week 33, Wednesday ,the close price is 29612 RMB\n",
+      "2017-08-17 is month 8 week 33, Thursday ,the close price is 28816 RMB\n",
+      "2017-08-18 is month 8 week 33, Friday ,the close price is 27752 RMB\n",
+      "2017-08-19 is month 8 week 33, Saturday ,the close price is 28062 RMB\n",
+      "2017-08-20 is month 8 week 33, Sunday ,the close price is 27416 RMB\n",
+      "2017-08-21 is month 8 week 34, Monday ,the close price is 26919 RMB\n",
+      "2017-08-22 is month 8 week 34, Tuesday ,the close price is 27565 RMB\n",
+      "2017-08-23 is month 8 week 34, Wednesday ,the close price is 27907 RMB\n",
+      "2017-08-24 is month 8 week 34, Thursday ,the close price is 29063 RMB\n",
+      "2017-08-25 is month 8 week 34, Friday ,the close price is 29305 RMB\n",
+      "2017-08-26 is month 8 week 34, Saturday ,the close price is 29168 RMB\n",
+      "2017-08-27 is month 8 week 34, Sunday ,the close price is 28945 RMB\n",
+      "2017-08-28 is month 8 week 35, Monday ,the close price is 29340 RMB\n",
+      "2017-08-29 is month 8 week 35, Tuesday ,the close price is 30656 RMB\n",
+      "2017-08-30 is month 8 week 35, Wednesday ,the close price is 30532 RMB\n",
+      "2017-08-31 is month 8 week 35, Thursday ,the close price is 31391 RMB\n",
+      "2017-09-01 is month 9 week 35, Friday ,the close price is 32482 RMB\n",
+      "2017-09-02 is month 9 week 35, Saturday ,the close price is 30470 RMB\n",
+      "2017-09-03 is month 9 week 35, Sunday ,the close price is 30336 RMB\n",
+      "2017-09-04 is month 9 week 36, Monday ,the close price is 28208 RMB\n",
+      "2017-09-05 is month 9 week 36, Tuesday ,the close price is 28918 RMB\n",
+      "2017-09-06 is month 9 week 36, Wednesday ,the close price is 30181 RMB\n",
+      "2017-09-07 is month 9 week 36, Thursday ,the close price is 30089 RMB\n",
+      "2017-09-08 is month 9 week 36, Friday ,the close price is 27976 RMB\n",
+      "2017-09-09 is month 9 week 36, Saturday ,the close price is 27818 RMB\n",
+      "2017-09-10 is month 9 week 36, Sunday ,the close price is 27359 RMB\n",
+      "2017-09-11 is month 9 week 37, Monday ,the close price is 27351 RMB\n",
+      "2017-09-12 is month 9 week 37, Tuesday ,the close price is 27111 RMB\n",
+      "2017-09-13 is month 9 week 37, Wednesday ,the close price is 25354 RMB\n",
+      "2017-09-14 is month 9 week 37, Thursday ,the close price is 21152 RMB\n",
+      "2017-09-15 is month 9 week 37, Friday ,the close price is 24164 RMB\n",
+      "2017-09-16 is month 9 week 37, Saturday ,the close price is 24111 RMB\n",
+      "2017-09-17 is month 9 week 37, Sunday ,the close price is 24057 RMB\n",
+      "2017-09-18 is month 9 week 38, Monday ,the close price is 26737 RMB\n",
+      "2017-09-19 is month 9 week 38, Tuesday ,the close price is 25652 RMB\n",
+      "2017-09-20 is month 9 week 38, Wednesday ,the close price is 25361 RMB\n",
+      "2017-09-21 is month 9 week 38, Thursday ,the close price is 23804 RMB\n",
+      "2017-09-22 is month 9 week 38, Friday ,the close price is 23761 RMB\n",
+      "2017-09-23 is month 9 week 38, Saturday ,the close price is 24908 RMB\n",
+      "2017-09-24 is month 9 week 38, Sunday ,the close price is 24216 RMB\n",
+      "2017-09-25 is month 9 week 39, Monday ,the close price is 26007 RMB\n",
+      "2017-09-26 is month 9 week 39, Tuesday ,the close price is 25869 RMB\n",
+      "2017-09-27 is month 9 week 39, Wednesday ,the close price is 27955 RMB\n",
+      "2017-09-28 is month 9 week 39, Thursday ,the close price is 27882 RMB\n",
+      "2017-09-29 is month 9 week 39, Friday ,the close price is 27711 RMB\n",
+      "2017-09-30 is month 9 week 39, Saturday ,the close price is 28969 RMB\n",
+      "2017-10-01 is month 10 week 39, Sunday ,the close price is 29264 RMB\n",
+      "2017-10-02 is month 10 week 40, Monday ,the close price is 29295 RMB\n",
+      "2017-10-03 is month 10 week 40, Tuesday ,the close price is 28743 RMB\n",
+      "2017-10-04 is month 10 week 40, Wednesday ,the close price is 28120 RMB\n",
+      "2017-10-05 is month 10 week 40, Thursday ,the close price is 28764 RMB\n",
+      "2017-10-06 is month 10 week 40, Friday ,the close price is 29084 RMB\n",
+      "2017-10-07 is month 10 week 40, Saturday ,the close price is 29521 RMB\n",
+      "2017-10-08 is month 10 week 40, Sunday ,the close price is 30583 RMB\n",
+      "2017-10-09 is month 10 week 41, Monday ,the close price is 31622 RMB\n",
+      "2017-10-10 is month 10 week 41, Tuesday ,the close price is 31243 RMB\n",
+      "2017-10-11 is month 10 week 41, Wednesday ,the close price is 31830 RMB\n",
+      "2017-10-12 is month 10 week 41, Thursday ,the close price is 35833 RMB\n",
+      "2017-10-13 is month 10 week 41, Friday ,the close price is 37106 RMB\n",
+      "2017-10-14 is month 10 week 41, Saturday ,the close price is 38222 RMB\n",
+      "2017-10-15 is month 10 week 41, Sunday ,the close price is 37517 RMB\n",
+      "2017-10-16 is month 10 week 42, Monday ,the close price is 37917 RMB\n",
+      "2017-10-17 is month 10 week 42, Tuesday ,the close price is 37060 RMB\n",
+      "2017-10-18 is month 10 week 42, Wednesday ,the close price is 36928 RMB\n",
+      "2017-10-19 is month 10 week 42, Thursday ,the close price is 37704 RMB\n",
+      "2017-10-20 is month 10 week 42, Friday ,the close price is 39634 RMB\n",
+      "2017-10-21 is month 10 week 42, Saturday ,the close price is 39827 RMB\n",
+      "2017-10-22 is month 10 week 42, Sunday ,the close price is 39673 RMB\n",
+      "2017-10-23 is month 10 week 43, Monday ,the close price is 39144 RMB\n",
+      "2017-10-24 is month 10 week 43, Tuesday ,the close price is 36611 RMB\n",
+      "2017-10-25 is month 10 week 43, Wednesday ,the close price is 38067 RMB\n",
+      "2017-10-26 is month 10 week 43, Thursday ,the close price is 39108 RMB\n",
+      "2017-10-27 is month 10 week 43, Friday ,the close price is 38353 RMB\n",
+      "2017-10-28 is month 10 week 43, Saturday ,the close price is 38122 RMB\n",
+      "2017-10-29 is month 10 week 43, Sunday ,the close price is 40925 RMB\n",
+      "2017-10-30 is month 10 week 44, Monday ,the close price is 40682 RMB\n",
+      "2017-10-31 is month 10 week 44, Tuesday ,the close price is 42779 RMB\n",
+      "2017-11-01 is month 11 week 44, Wednesday ,the close price is 44572 RMB\n",
+      "2017-11-02 is month 11 week 44, Thursday ,the close price is 46462 RMB\n",
+      "2017-11-03 is month 11 week 44, Friday ,the close price is 47518 RMB\n",
+      "2017-11-04 is month 11 week 44, Saturday ,the close price is 49047 RMB\n",
+      "2017-11-05 is month 11 week 44, Sunday ,the close price is 48907 RMB\n",
+      "2017-11-06 is month 11 week 45, Monday ,the close price is 46159 RMB\n",
+      "2017-11-07 is month 11 week 45, Tuesday ,the close price is 47249 RMB\n",
+      "2017-11-08 is month 11 week 45, Wednesday ,the close price is 49427 RMB\n",
+      "2017-11-09 is month 11 week 45, Thursday ,the close price is 47448 RMB\n",
+      "2017-11-10 is month 11 week 45, Friday ,the close price is 43637 RMB\n",
+      "2017-11-11 is month 11 week 45, Saturday ,the close price is 42085 RMB\n",
+      "2017-11-12 is month 11 week 45, Sunday ,the close price is 38904 RMB\n",
+      "2017-11-13 is month 11 week 46, Monday ,the close price is 43279 RMB\n",
+      "2017-11-14 is month 11 week 46, Tuesday ,the close price is 43801 RMB\n",
+      "2017-11-15 is month 11 week 46, Wednesday ,the close price is 48232 RMB\n",
+      "2017-11-16 is month 11 week 46, Thursday ,the close price is 52012 RMB\n",
+      "2017-11-17 is month 11 week 46, Friday ,the close price is 50973 RMB\n",
+      "2017-11-18 is month 11 week 46, Saturday ,the close price is 51542 RMB\n",
+      "2017-11-19 is month 11 week 46, Sunday ,the close price is 53279 RMB\n",
+      "2017-11-20 is month 11 week 47, Monday ,the close price is 54656 RMB\n",
+      "2017-11-21 is month 11 week 47, Tuesday ,the close price is 53673 RMB\n",
+      "2017-11-22 is month 11 week 47, Wednesday ,the close price is 54403 RMB\n",
+      "2017-11-23 is month 11 week 47, Thursday ,the close price is 52676 RMB\n",
+      "2017-11-24 is month 11 week 47, Friday ,the close price is 54136 RMB\n",
+      "2017-11-25 is month 11 week 47, Saturday ,the close price is 57851 RMB\n",
+      "2017-11-26 is month 11 week 47, Sunday ,the close price is 60980 RMB\n",
+      "2017-11-27 is month 11 week 48, Monday ,the close price is 64246 RMB\n",
+      "2017-11-28 is month 11 week 48, Tuesday ,the close price is 65458 RMB\n",
+      "2017-11-29 is month 11 week 48, Wednesday ,the close price is 64890 RMB\n",
+      "2017-11-30 is month 11 week 48, Thursday ,the close price is 65583 RMB\n",
+      "2017-12-01 is month 12 week 48, Friday ,the close price is 71825 RMB\n",
+      "2017-12-02 is month 12 week 48, Saturday ,the close price is 72079 RMB\n",
+      "2017-12-03 is month 12 week 48, Sunday ,the close price is 74007 RMB\n",
+      "2017-12-04 is month 12 week 49, Monday ,the close price is 76852 RMB\n",
+      "2017-12-05 is month 12 week 49, Tuesday ,the close price is 77398 RMB\n",
+      "2017-12-06 is month 12 week 49, Wednesday ,the close price is 90679 RMB\n",
+      "2017-12-07 is month 12 week 49, Thursday ,the close price is 111589 RMB\n",
+      "2017-12-08 is month 12 week 49, Friday ,the close price is 106233 RMB\n",
+      "2017-12-09 is month 12 week 49, Saturday ,the close price is 98676 RMB\n",
+      "2017-12-10 is month 12 week 49, Sunday ,the close price is 99525 RMB\n",
+      "2017-12-11 is month 12 week 50, Monday ,the close price is 110642 RMB\n",
+      "2017-12-12 is month 12 week 50, Tuesday ,the close price is 113732 RMB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import Ipynb_importer\n",
+    "\n",
+    "import json\n",
+    "\n",
+    "#将数据加载到一个列表中\n",
+    "filename = 'btc_close_2017.json'\n",
+    "with open(filename) as f:\n",
+    "    btc_data = json.load(f)\n",
+    "#print(btc_data)\n",
+    "#打印每一天的信息\n",
+    "for btc_dict in btc_data:\n",
+    "    date = btc_dict['date']\n",
+    "    month = int(btc_dict['month'])\n",
+    "    week = int(btc_dict['week'])\n",
+    "    weekday = btc_dict['weekday']\n",
+    "    close = int(float(btc_dict['close']))\n",
+    "    print(\"{} is month {} week {}, {} ,the close price is {} RMB\".format(date, month, week, weekday, close))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0f332a0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import Ipynb_importer\n",
+    "\n",
+    "import json\n",
+    "import pygal\n",
+    "import math\n",
+    "\n",
+    "#将数据加载到一个列表中\n",
+    "filename = 'btc_close_2017.json'\n",
+    "with open(filename) as f:\n",
+    "    btc_data = json.load(f)\n",
+    "#print(btc_data)\n",
+    "\n",
+    "#创建5个列表，分别储存日期和收盘价\n",
+    "dates = []\n",
+    "months = []\n",
+    "weeks = []\n",
+    "weekdays = []\n",
+    "close = []\n",
+    "#打印每一天的信息\n",
+    "for btc_dict in btc_data:\n",
+    "    dates.append(btc_dict['date'])\n",
+    "    months.append(int(btc_dict['month']))\n",
+    "    weeks.append(int(btc_dict['week']))\n",
+    "    weekdays.append(btc_dict['weekday'])\n",
+    "    close.append(int(float(btc_dict['close'])))\n",
+    "    #print(\"{} is month {} week {}, {} ,the close price is {} RMB\".format(date, month, week, weekday, close))\n",
+    "    \n",
+    "line_chart = pygal.Line(x_label_rotation=20, show_minor_x_labels=False)\n",
+    "line_chart.title = '收盘价（￥）'\n",
+    "line_chart.x_labels = dates\n",
+    "N = 20 # x轴坐标每隔20天显示一次\n",
+    "line_chart.x_labels_major = dates[::N]\n",
+    "close_log = [math.log10(_) for _ in close]\n",
+    "line_chart.add('log收盘价', close_log)\n",
+    "line_chart.render_to_file('收盘价对数变换折线图（￥）.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5ccdab3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import Ipynb_importer\n",
+    "\n",
+    "import json\n",
+    "import pygal\n",
+    "import math\n",
+    "\n",
+    "from itertools import groupby\n",
+    "\n",
+    "#将数据加载到一个列表中\n",
+    "filename = 'btc_close_2017.json'\n",
+    "with open(filename) as f:\n",
+    "    btc_data = json.load(f)\n",
+    "#print(btc_data)\n",
+    "\n",
+    "#创建5个列表，分别储存日期和收盘价\n",
+    "dates = []\n",
+    "months = []\n",
+    "weeks = []\n",
+    "weekdays = []\n",
+    "close = []\n",
+    "#打印每一天的信息\n",
+    "for btc_dict in btc_data:\n",
+    "    dates.append(btc_dict['date'])\n",
+    "    months.append(int(btc_dict['month']))\n",
+    "    weeks.append(int(btc_dict['week']))\n",
+    "    weekdays.append(btc_dict['weekday'])\n",
+    "    close.append(int(float(btc_dict['close'])))\n",
+    "    \n",
+    "def draw_line(x_data, y_data, title, y_legend):\n",
+    "    xy_map = []\n",
+    "    for x, y in groupby(sorted(zip(x_data, y_data)), key=lambda _: _[0]):\n",
+    "        y_list = [v for _, v in y]\n",
+    "        xy_map.append([x, sum(y_list) / len(y_list)])\n",
+    "        \n",
+    "    x_unique, y_mean = [*zip(*xy_map)]\n",
+    "    line_chart = pygal.Line()\n",
+    "    line_chart.title = title \n",
+    "    line_chart.x_labels = x_unique\n",
+    "    line_chart.add(y_legend, y_mean)\n",
+    "    line_chart.render_to_file(title+' .svg')\n",
+    "    return line_chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f6bf8ad8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'cairosvg'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32mD:\\Program Files (x86)\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mD:\\Program Files (x86)\\anaconda3\\lib\\site-packages\\pygal\\graph\\base.py\u001b[0m in \u001b[0;36m_repr_png_\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    233\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_repr_png_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m         \u001b[1;34m\"\"\"Display png in IPython notebook\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 235\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrender_to_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mD:\\Program Files (x86)\\anaconda3\\lib\\site-packages\\pygal\\graph\\public.py\u001b[0m in \u001b[0;36mrender_to_png\u001b[1;34m(self, filename, dpi, **kwargs)\u001b[0m\n\u001b[0;32m    116\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrender_to_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m72\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m         \u001b[1;34m\"\"\"Render the graph, convert it to png and write it to filename\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 118\u001b[1;33m         \u001b[1;32mimport\u001b[0m \u001b[0mcairosvg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    119\u001b[0m         return cairosvg.svg2png(\n\u001b[0;32m    120\u001b[0m             \u001b[0mbytestring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrite_to\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cairosvg'"
+     ]
+    },
+    {
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0\" class=\"tooltip\"><rect rx=\"0\" ry=\"0\" width=\"0\" height=\"0\" class=\"tooltip-box\"/><g class=\"text\"/></g></g><g transform=\"translate(10, 56)\" class=\"legends\"><g id=\"activate-serie-0\" class=\"legend reactive activate-serie\"><rect x=\"0.0\" y=\"1.0\" width=\"12\" height=\"12\" class=\"color-0 reactive\"/><text x=\"17.0\" y=\"11.2\">月日均值</text></g></g><g transform=\"translate(790, 56)\" class=\"legends\"/></g></svg>",
+      "text/plain": [
+       "<pygal.graph.line.Line at 0x164e9d518b0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idx_month = dates.index('2017-12-01')\n",
+    "line_chart_month = draw_line(months[:idx_month], close[:idx_month], '收盘价月日均值 （￥）',\n",
+    "                            '月日均值')\n",
+    "line_chart_month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fb49e10c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'cairosvg'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32mD:\\Program Files (x86)\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mD:\\Program Files (x86)\\anaconda3\\lib\\site-packages\\pygal\\graph\\base.py\u001b[0m in \u001b[0;36m_repr_png_\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    233\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_repr_png_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m         \u001b[1;34m\"\"\"Display png in IPython notebook\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 235\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrender_to_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mD:\\Program Files (x86)\\anaconda3\\lib\\site-packages\\pygal\\graph\\public.py\u001b[0m in \u001b[0;36mrender_to_png\u001b[1;34m(self, filename, dpi, **kwargs)\u001b[0m\n\u001b[0;32m    116\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrender_to_png\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m72\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m         \u001b[1;34m\"\"\"Render the graph, convert it to png and write it to filename\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 118\u001b[1;33m         \u001b[1;32mimport\u001b[0m \u001b[0mcairosvg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    119\u001b[0m         return cairosvg.svg2png(\n\u001b[0;32m    120\u001b[0m             \u001b[0mbytestring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrite_to\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'cairosvg'"
+     ]
+    },
+    {
+     "data": {
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transform=\"translate(115, 46)\" class=\"plot tooltip-overlay\"><g transform=\"translate(0 0)\" style=\"opacity: 0\" class=\"tooltip\"><rect rx=\"0\" ry=\"0\" width=\"0\" height=\"0\" class=\"tooltip-box\"/><g class=\"text\"/></g></g><g transform=\"translate(10, 56)\" class=\"legends\"><g id=\"activate-serie-0\" class=\"legend reactive activate-serie\"><rect x=\"0.0\" y=\"1.0\" width=\"12\" height=\"12\" class=\"color-0 reactive\"/><text x=\"17.0\" y=\"11.2\">周日均值</text></g></g><g transform=\"translate(790, 56)\" class=\"legends\"/></g></svg>",
+      "text/plain": [
+       "<pygal.graph.line.Line at 0x164e9d51250>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idx_week = dates.index('2017-12-11')\n",
+    "line_chart_week = draw_line(weeks[1:idx_week], close[1:idx_week],\n",
+    "                           '收盘价周日均值（￥）', '周日均值')\n",
+    "line_chart_week"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "38ec0202",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx_week = dates.index('2017-12-11')\n",
+    "wd = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
+    "weekdays_int = [wd.index(w) + 1 for w in weekdays[1:idx_week]]\n",
+    "line_chart_weekday = draw_line(weekdays_int, close[1:idx_week], \n",
+    "                              '收盘价星期均值（￥）', '星期均值')\n",
+    "line_chart_weekday.x_labels = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']\n",
+    "line_chart_weekday.render_to_file('收盘价星期均值（￥）.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9786ffc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('收盘价Dashboard.html', 'w', encoding='utf8') as html_file:\n",
+    "    html_file.write('<html><head><title>收盘价Dashboard</title><metacharset=\"utf-8\"></head><body>\\n')\n",
+    "    for svg in [\n",
+    "        '收盘价折线图（￥）.svg', '收盘价对数变换折线图（￥）.svg',\n",
+    "        '收盘价月日均值 （￥） .svg','收盘价周日均值（￥） .svg',\n",
+    "        '收盘价星期均值（￥） .svg'\n",
+    "    ]:\n",
+    "        html_file.write('    <object type=\"image/svg+xml\" data=\"{0}\"height=500></object>\\n'.format(svg))\n",
+    "    html_file.write('</body></html>')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c7ebfbb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb7608c2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa516833",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99b47886",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10 (default, Jun 22 2022, 20:18:18) \n[GCC 9.4.0]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

matplotlib_dataScience/btc_close_2017.json

+[{
+    "date": "2017-01-01",
+    "month": "01",
+    "week": "52",
+    "weekday": "Sunday",
+    "close": "6928.6492"
+},
+{
+    "date": "2017-01-02",
+    "month": "01",
+    "week": "1",
+    "weekday": "Monday",
+    "close": "7070.2554"
+},
+{
+    "date": "2017-01-03",
+    "month": "01",
+    "week": "1",
+    "weekday": "Tuesday",
+    "close": "7175.1082"
+},
+{
+    "date": "2017-01-04",
+    "month": "01",
+    "week": "1",
+    "weekday": "Wednesday",
+    "close": "7835.7615"
+},
+{
+    "date": "2017-01-05",
+    "month": "01",
+    "week": "1",
+    "weekday": "Thursday",
+    "close": "6928.7578"
+},
+{
+    "date": "2017-01-06",
+    "month": "01",
+    "week": "1",
+    "weekday": "Friday",
+    "close": "6196.6928"
+},
+{
+    "date": "2017-01-07",
+    "month": "01",
+    "week": "1",
+    "weekday": "Saturday",
+    "close": "6262.1471"
+},
+{
+    "date": "2017-01-08",
+    "month": "01",
+    "week": "1",
+    "weekday": "Sunday",
+    "close": "6319.9404"
+},
+{
+    "date": "2017-01-09",
+    "month": "01",
+    "week": "2",
+    "weekday": "Monday",
+    "close": "6239.1506"
+},
+{
+    "date": "2017-01-10",
+    "month": "01",
+    "week": "2",
+    "weekday": "Tuesday",
+    "close": "6263.1548"
+},
+{
+    "date": "2017-01-11",
+    "month": "01",
+    "week": "2",
+    "weekday": "Wednesday",
+    "close": "5383.0598"
+},
+{
+    "date": "2017-01-12",
+    "month": "01",
+    "week": "2",
+    "weekday": "Thursday",
+    "close": "5566.7345"
+},
+{
+    "date": "2017-01-13",
+    "month": "01",
+    "week": "2",
+    "weekday": "Friday",
+    "close": "5700.0716"
+},
+{
+    "date": "2017-01-14",
+    "month": "01",
+    "week": "2",
+    "weekday": "Saturday",
+    "close": "5648.6897"
+},
+{
+    "date": "2017-01-15",
+    "month": "01",
+    "week": "2",
+    "weekday": "Sunday",
+    "close": "5674.7977"
+},
+{
+    "date": "2017-01-16",
+    "month": "01",
+    "week": "3",
+    "weekday": "Monday",
+    "close": "5730.0658"
+},
+{
+    "date": "2017-01-17",
+    "month": "01",
+    "week": "3",
+    "weekday": "Tuesday",
+    "close": "6202.9704"
+},
+{
+    "date": "2017-01-18",
+    "month": "01",
+    "week": "3",
+    "weekday": "Wednesday",
+    "close": "6047.6601"
+},
+{
+    "date": "2017-01-19",
+    "month": "01",
+    "week": "3",
+    "weekday": "Thursday",
+    "close": "6170.8433"
+},
+{
+    "date": "2017-01-20",
+    "month": "01",
+    "week": "3",
+    "weekday": "Friday",
+    "close": "6131.2511"
+},
+{
+    "date": "2017-01-21",
+    "month": "01",
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matplotlib_dataScience/btc_close_2017_request.json

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matplotlib_dataScience/btc_close_2017_urllib.json

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\ No newline at end of file

matplotlib_dataScience/death_valley_2014.csv

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matplotlib_dataScience/sitka_weather_2014.csv

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+2014-4-14,46,42,39,42,39,33,93,86,73,30.07,29.86,29.79,10,10,8,10,3,,0.10,8,Rain,302
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+2014-4-17,48,45,42,42,39,34,92,80,63,29.57,29.39,29.28,10,10,5,12,8,,0.07,8,Rain,312
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+2014-5-8,57,50,44,45,42,39,93,76,53,29.76,29.70,29.67,10,10,9,8,4,,0.05,7,Rain,311
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+2014-5-12,57,51,45,46,44,41,93,73,55,30.27,30.23,30.20,10,10,10,13,5,,0.01,7,Rain,111
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