一、pandas匯入資料&matplotlib基本繪圖

# _*_ coding: utf-8 _*_

import pandas as pd
import matplotlib.pyplot as plt

#
# Author: yz
# Date: 2017-12-3
#

'''
pandas匯入資料和date格式轉換
matplotlib基本的繪圖：橫縱座標標籤，標題，座標值旋轉等
'''


# 匯入資料
unrate = pd.read_csv("data/UNRATE.csv")
unrate["DATE"] = pd.to_datetime(unrate["DATE"
 
])  # 1948/1/1 -> 1948-01-01
# print(unrate.head(10))
'''
        DATE  VALUE
0 1948-01-01    3.4
1 1948-02-01    3.8
2 1948-03-01    4.0
3 1948-04-01    3.9
4 1948-05-01    3.5
5 1948-06-01    3.6
6 1948-07-01    3.6
7 1948-08-01    3.9
8 1948-09-01    3.8
9 1948-10-01    3.7
'''

# plt.plot()
# plt.show()

'''
While the y-axis looks fine, the x-axis tick labels are too close together and are unreadable
We can rotate the x-axis tick labels by 90 degrees so they don't overlap
We can specify degrees of rotation using a float or integer value.
'''
 

# first_twelve = unrate[0:12]
# plt.plot(first_twelve["DATE"], first_twelve["VALUE"])
# plt.xticks(rotation=45) # x座標值太長時，可以讓其旋轉再顯示
# # print(help(plt.xticks))
# plt.show()


'''
xlabel(): accepts a string value, which gets set as the x-axis label.
ylabel(): accepts a string value, which is set as the y-axis label.
title(): accepts a string value, which is set as the plot title.
'''
 

first_twelve = unrate[0:12]
plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
plt.xticks(rotation=45)
plt.xlabel('Month')
plt.ylabel('Unemployment Rate')
plt.title('Monthly Unemployment Trends, 1948')
plt.show()

二、增加子圖&指定影象大小和線條顏色&新增標籤並指定位置

# _*_ coding: utf-8 _*_

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

#
# Author: yz
# Date: 2017-12-3
#
'''
增加子圖 add_subplot(first,second,index)
指定影象大小 plt.figure(figsize=(12, 6)) 
指定線條的顏色 plt.plot(unrate[0:12]["MONTH"], unrate[0:12]["VALUE"], c="red")
新增標籤並指定位置 plt.plot(subset["MONTH"], subset["VALUE"], c=colors[i], label=label) plt.legend(loc='upper left')
'''


'''
add_subplot(first,second,index) first means number of Row,second means number of Column.
'''
# fig = plt.figure()
# ax1 = fig.add_subplot(3, 2, 1)
# ax2 = fig.add_subplot(3, 2, 2)
# ax3 = fig.add_subplot(3, 2, 3)
# ax6 = fig.add_subplot(3, 2, 6)
# ax1.plot(np.random.randint(1, 5, 5), np.arange(5))
# ax2.plot(np.arange(10) * 3, np.arange(10))
# plt.show()


'''
指定顏色和大小
'''
unrate = pd.read_csv("data/UNRATE.csv")
unrate["DATE"] = pd.to_datetime(unrate["DATE"])
unrate["MONTH"] = unrate["DATE"].dt.month

# fig = plt.figure(figsize=(12, 6))   # figsize 影象大小
# plt.plot(unrate[0:12]["MONTH"], unrate[0:12]["VALUE"], c="red") # c 指定線條顏色
# plt.plot(unrate[12:24]["MONTH"], unrate[12:24]["VALUE"], c="green")
# plt.show()


'''
lable
'''
fig = plt.figure(figsize=(10, 6))
colors = ['red', 'blue', 'green', 'orange', 'black']
for i in range(5):
    start_index = i * 12
    end_index = (i + 1) * 12
    label = str(1948 + i)
    subset = unrate[start_index:end_index]
    plt.plot(subset["MONTH"], subset["VALUE"], c=colors[i], label=label)    # 新增lable
# 指定lable的位置 'best' 'upper right/left'  'lower right/left' 'right' 'center right/left' 'upper/lower center' 'center'
plt.legend(loc='upper left')
# print(help(plt.legend))
plt.xlabel('Month, Integer')
plt.ylabel('Unemployment Rate, Percent')
plt.title('Monthly Unemployment Trends, 1948-1952')
plt.show()

三、柱狀圖&散點圖

# _*_ coding: utf-8 _*_

import pandas as pd
import matplotlib.pyplot as plt
from numpy import arange

#
# Author: yz
# Date: 2017-12-3
#

'''
柱狀圖 fig, ax = plt.subplots()  ax.bar(bar_positions, bar_heights, 0.5)
散點圖
'''

#
# 匯入資料
#
reviews = pd.read_csv("data/fandango_scores.csv")
cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
norm_reviews = reviews[cols]
# print(norm_reviews[:1])

#
# 柱狀圖
#
# num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
# bar_heights = norm_reviews.ix[0, num_cols].values
# # print(bar_heights)  # [4.2999999999999998 3.5499999999999998 3.8999999999999999 4.5 5.0]
# bar_positions = arange(5) + 0.75
# # print(bar_positions)    # [1 2 3 4 5]
# fig, ax = plt.subplots()
# ax.bar(bar_positions, bar_heights, 0.5)
# plt.show()

#
# 將座標值改為標籤 ax.set_xticklabels(num_cols, rotation=45)
#
# num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
# bar_heights = norm_reviews.ix[0, num_cols].values
# # print(bar_heights)  # [4.2999999999999998 3.5499999999999998 3.8999999999999999 4.5 5.0]
# bar_positions = arange(5) + 0.75
# # print(bar_positions)    # [1 2 3 4 5]
# fig, ax = plt.subplots()
# ax.bar(bar_positions, bar_heights, 0.5)
#
# tick_positions = range(1,6)
# ax.set_xticks(tick_positions)
# ax.set_xticklabels(num_cols, rotation=45)
#
# ax.set_xlabel('Rating Source')
# ax.set_ylabel('Average Rating')
# ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
# plt.show()

# fig, ax = plt.subplots()
# # ax.hist(norm_reviews['Fandango_Ratingvalue'])
# # ax.hist(norm_reviews['Fandango_Ratingvalue'],bins=20)
# ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(3, 5),bins=20)
# plt.show()


#
# 散點圖
#
fig, ax = plt.subplots()
ax.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm'])
# ax.scatter([4.5, 3], [4.3, 4])    # （4.5， 4.3） （3， 4）
ax.set_xlabel('Fandango')
ax.set_ylabel('Rotten Tomatoes')
plt.show()

曲線圖&座標的設定

# _*_ coding: utf-8 _*_

import pandas as pd
import matplotlib.pyplot as plt

#
# Author: yz
# Date: 2017-12-3
#

'''
曲線圖
'''

women_degrees = pd.read_csv('data/percent-bachelors-degrees-women-usa.csv')
# plt.plot(women_degrees['Year'], women_degrees['Biology'])
# plt.show()


# plt.plot(women_degrees['Year'], women_degrees['Biology'], c='blue', label='Women')
# plt.plot(women_degrees['Year'], 100-women_degrees['Biology'], c='green', label='Men')
# plt.legend(loc='upper right')
# plt.title('Percentage of Biology Degrees Awarded By Gender')
# plt.show()



# fig, ax = plt.subplots()
# ax.plot(women_degrees['Year'], women_degrees['Biology'], label='Women')
# ax.plot(women_degrees['Year'], 100-women_degrees['Biology'], label='Men')
#
# ax.tick_params(bottom="off", top="off", left="off", right="off")
# ax.set_title('Percentage of Biology Degrees Awarded By Gender')
# ax.legend(loc="upper right")
#
# plt.show()


# fig, ax = plt.subplots()
# ax.plot(women_degrees['Year'], women_degrees['Biology'], c='blue', label='Women')
# ax.plot(women_degrees['Year'], 100-women_degrees['Biology'], c='green', label='Men')
# ax.tick_params(bottom="off", top="off", left="off", right="off")
#
# for key,spine in ax.spines.items():
#     spine.set_visible(False)
# # End solution code.
# ax.legend(loc='upper right')
# plt.show()


major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']
fig = plt.figure(figsize=(12, 12))

for sp in range(0,4):
    ax = fig.add_subplot(2,2,sp+1)
    ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c='blue', label='Women')
    ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c='green', label='Men')
    # Add your code here.

# Calling pyplot.legend() here will add the legend to the last subplot that was created.
plt.legend(loc='upper right')
plt.show()

major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']
fig = plt.figure(figsize=(12, 12))

for sp in range(0,4):
    ax = fig.add_subplot(2,2,sp+1)
    ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c='blue', label='Women')
    ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c='green', label='Men')
    for key,spine in ax.spines.items():
        spine.set_visible(False)
    ax.set_xlim(1968, 2011)
    ax.set_ylim(0,100)
    ax.set_title(major_cats[sp])
    ax.tick_params(bottom="off", top="off", left="off", right="off")

# Calling pyplot.legend() here will add the legend to the last subplot that was created.
plt.legend(loc='upper right')
plt.show()

線條的種類

# _*_ coding: utf-8 _*_

import pandas as pd
import matplotlib.pyplot as plt

#
# Author: yz
# Date: 2017-12-3
#


women_degrees = pd.read_csv('data/percent-bachelors-degrees-women-usa.csv')
major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']


# cb_dark_blue = (0/255, 107/255, 164/255)
# cb_orange = (255/255, 128/255, 14/255)
#
# fig = plt.figure(figsize=(12, 12))
#
# for sp in range(0,4):
#     ax = fig.add_subplot(2,2,sp+1)
#     # The color for each line is assigned here.
#     ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women')
#     ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men')
#     for key,spine in ax.spines.items():
#         spine.set_visible(False)
#     ax.set_xlim(1968, 2011)
#     ax.set_ylim(0,100)
#     ax.set_title(major_cats[sp])
#     ax.tick_params(bottom="off", top="off", left="off", right="off")
#
# plt.legend(loc='upper right')
# plt.show()


#Setting Line Width
cb_dark_blue = (0/255, 107/255, 164/255)
cb_orange = (255/255, 128/255, 14/255)

fig = plt.figure(figsize=(12, 12))

for sp in range(0,4):
    ax = fig.add_subplot(2,2,sp+1)
    # Set the line width when specifying how each line should look.
    ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women', linewidth=10)
    ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men', linewidth=10)
    for key,spine in ax.spines.items():
        spine.set_visible(False)
    ax.set_xlim(1968, 2011)
    ax.set_ylim(0,100)
    ax.set_title(major_cats[sp])
    ax.tick_params(bottom="off", top="off", left="off", right="off")

plt.legend(loc='upper right')
plt.show()