keyword_analytic_web/archived/test/treemap7.py

# #import some lib
# import numpy as np # linear algebra
# import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# import matplotlib.pyplot as plt
# import seaborn as sns
#
# color = sns.color_palette()
# # %matplotlib inline
# pd.options.mode.chained_assignment = None# default='warn'
# order_products_prior_df = pd.read_csv("../docs/keyword.csv")
# # aisles_df = pd.read_csv("../input/aisles.csv")
# # departments_df = pd.read_csv("../input/departments.csv")
# # order_products_prior_df = pd.merge(products_df, aisles_df, on='aisle_id', how='left')
# # order_products_prior_df = pd.merge(order_products_prior_df, departments_df, on='department_id', how='left')
# order_products_prior_df.head()
#
# import matplotlib
# import squarify
#
# temp = order_products_prior_df[['keyword','Avg_monthly_searches']]
# temp = pd.concat([
# order_products_prior_df.groupby('keyword')['Avg_monthly_searches'].nunique().rename('Avg_monthly_searches')
# # order_products_prior_df.groupby('department')['aisle'].nunique().rename('aisle_department')
# ], axis=1).reset_index()
# temp = temp.set_index('keyword')
# temp2 = temp.sort_values(by="Avg_monthly_searches", ascending=False)
#
# # TreeMap parameters
# x = 0.
# y = 0.
# width = 100.
# height = 100.
# cmap = matplotlib.cm.viridis
#
# # color scale on the population
# # min and max values without Pau
# mini, maxi = temp2.products_department.min(), temp2.products_department.max()
# norm = matplotlib.colors.Normalize(vmin=mini, vmax=maxi)
# colors = [cmap(norm(value)) for value in temp2.products_department]
# colors[1] = "#FBFCFE"
#
# # labels for squares
# labels = ["%s/n%d search num keyword num" % (label)
#           for label in zip(temp2.index, temp2.Avg_monthly_searches)]
#
# # make plot
# fig = plt.figure(figsize=(12, 10))
# fig.suptitle("search keyword", fontsize=20)
# ax = fig.add_subplot(111, aspect="equal")
# ax = squarify.plot(temp2.Avg_monthly_searches, color=colors, label=labels, ax=ax, alpha=.7)
# ax.set_xticks([])
# ax.set_yticks([])
# # color bar
# # create dummy invisible image with a color map
# img = plt.imshow([temp2.Avg_monthly_searches], cmap=cmap)
# img.set_visible(False)
# fig.colorbar(img, orientation="vertical", shrink=.96)
# fig.text(.76, .9, "numbers of products", fontsize=14)
# fig.text(.5, 0.1,
# "powered by CJ /n keyword totale %d" % (temp2.Avg_monthly_searches.sum()), fontsize=14, ha="center")
# fig.text(.5, 0.07,
# "Source : http://netfly",
# fontsize=14,
# ha="center")
# plt.show()

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['Taipei Sans TC Beta']

sales = [100, 80, 50]
x_labels = ['A品牌', 'B品牌', 'C品牌']

plt.bar(x_labels, sales)
plt.show()