直播數據分析
針對douyu_60937 直播間 (2018/11/19 19:04:18 - 2018/11/20 7:56:42) 這個時間段的數據分析
基礎數據展示
以上數據是從直播間的彈幕中提取的相關數據,每個字段解釋爲
{
'id': '唯一標識',
'user': '用戶名',
'cont': '發送的信息',
'level': '用戶等級',
'sign': '牌子',
'sign_leve': '牌子等級',
'rid': '發言房間號',
}依賴
pandas==0.23.4
matplotlib==3.0.2
numpy==1.15.4
datetime數據處理
基礎準備
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime
path = "douyu_60937.xlsx"
data = pd.read_excel(path)
根據牌子名稱統計最大值、最小值、平均值
- 計算牌子的最大值、最小值、平均值的時候需要根據user 將數據刪除重複項 ,避免多次計算
- 需要做成圖我們返回值設置成dict()
t = data[['user', 'sign', 'sign_leve']].drop_duplicates(subset=['user']) # 刪除重複用戶
t = data.groupby('sign').sign_leve.agg(['mean', 'min', 'max'])
t.sort_values(['max'], ascending=False, inplace=True)
print(t)
print(t[:20].to_dict())
{'mean': {'小人蔘': 11.848837209302326, '196': 16.475254730713246, '女流': 11.418693982074263, 'Amss': 10.0, '水煮肉': 26.0, '小殭屍': 9.93750495049505, '339': 18.941176470588236, '金髮雅': 24.0, '猛男': 7.768361581920904, '小豆包': 12.676724137931034, '阿冷': 10.416666666666666, '寅子': 7.059241706161138, '小癢蟲': 10.718562874251496, 'S1un': 22.0, '小緑帽': 9.938271604938272, '集團軍': 7.909323116219668, '小烏賊': 8.377464788732395, '點子王': 5.686131386861314, '小肚皮': 7.034911587538053, '林Q': 21.0}, 'min': {'小人蔘': 2, '196': 4, '女流': 3, 'Amss': 4, '水煮肉': 26, '小殭屍': 1, '339': 12, '金髮雅': 24, '猛男': 1, '小豆包': 3, '阿冷': 6, '寅子': 1, '小癢蟲': 2, 'S1un': 22, '小緑帽': 1, '集團軍': 1, '小烏賊': 1, '點子王': 3, '小肚皮': 1, '林Q': 21}, 'max': {'小人蔘': 30, '196': 30, '女流': 30, 'Amss': 27, '水煮肉': 26, '小殭屍': 25, '339': 25, '金髮雅': 24, '猛男': 23, '小豆包': 23, '阿冷': 22, '寅子': 22, '小癢蟲': 22, 'S1un': 22, '小緑帽': 22, '集團軍': 22, '小烏賊': 22, '點子王': 22, '小肚皮': 21, '林Q': 21}}根據牌子名稱統計數量
- 分類統計的時候我們要修改列名用rename(column={'老列名':'新列名'})
sign_max_count = self.data[['user', 'sign', 'sign_leve']].drop_duplicates(subset=['user']) # 刪除重複用戶
sign_max_count = self.data[['sign']].groupby(['sign']).agg({'sign': 'count'}).rename(
columns={'sign': 'sign_count'})
sign_max_count.sort_values(['sign_count'], ascending=False, inplace=True)
print(sign_max_count)
print(sign_max_count[:20].to_dict())
{'sign_count': {'小肚皮': 15439, '小殭屍': 12625, '集團軍': 1566, '196': 1374, '女流': 781, '影魔王': 640, '大馬猴': 429, '寅子': 422, '小緑帽': 405, '小8路': 397, '小烈驢': 370, '小烏賊': 355, '小贏僧': 355, '保安團': 343, '豬芳芳': 243, '小豆包': 232, '王菠蘿': 187, '二帆': 184, '猛男': 177, '汽車人': 173}}
統計各個等級的用戶數量
now_data = self.data[['user', 'level']].drop_duplicates(subset=['user']) # 刪除重複用戶
now_data = self.data[['level']].groupby(['level']).agg({'level': 'count'}).rename(
columns={'level': 'level_count'})
now_data.sort_values(['level_count'], ascending=False, inplace=True)
{'level_count': {16: 3159, 19: 3122, 17: 3086, 21: 2984, 18: 2882, 15: 2832, 22: 2624, 23: 2564, 20: 2545, 13: 2379, 14: 2308, 24: 2274, 11: 2066, 12: 1894, 7: 1781, 9: 1753, 10: 1690, 8: 1678, 5: 1645, 25: 1554}}每小時發言數量
- 根據時間統計需要構造一個datetime 數據類型的列 , 利用 resample("時間標識符") + count() 進行統計
- 爲了後續製圖方便我在這裏直接把 <class 'pandas._libs.tslibs.timestamps.Timestamp'> 轉換成python 內置的 datetime 類 方法是 to_pydatetime
df = self.data
df = df.loc[:, ('cont', 'uptime')]
df = df.set_index('uptime')
result = df.resample('H').count().rename(columns={'cont': 'user_count'})
print(result)
result = result.to_dict()
s = {}
for k, v in result['user_count'].items():
s[k.to_pydatetime()] = v
result['user_count'] = s
print(result)
return result
{'user_count': {datetime.datetime(2018, 11, 19, 19, 0): 12707, datetime.datetime(2018, 11, 19, 20, 0): 12374, datetime.datetime(2018, 11, 19, 21, 0): 19340, datetime.datetime(2018, 11, 19, 22, 0): 13530, datetime.datetime(2018, 11, 19, 23, 0): 8, datetime.datetime(2018, 11, 20, 0, 0): 2, datetime.datetime(2018, 11, 20, 1, 0): 1, datetime.datetime(2018, 11, 20, 2, 0): 0, datetime.datetime(2018, 11, 20, 3, 0): 0, datetime.datetime(2018, 11, 20, 4, 0): 5, datetime.datetime(2018, 11, 20, 5, 0): 1, datetime.datetime(2018, 11, 20, 6, 0): 11, datetime.datetime(2018, 11, 20, 7, 0): 23}}每小時在線人數(發言人)
df = self.data.drop_duplicates(subset=['user'])
df = df.loc[:, ('user', 'uptime')]
df = df.set_index('uptime')
result = df.resample('H').count().rename(columns={'user': 'user_count'})
print(result)
result = result.to_dict()
s = {}
for k, v in result['user_count'].items():
s[k.to_pydatetime()] = v
result['user_count'] = s
print(result)
{'user_count': {datetime.datetime(2018, 11, 19, 19, 0): 4223, datetime.datetime(2018, 11, 19, 20, 0): 2207, datetime.datetime(2018, 11, 19, 21, 0): 3843, datetime.datetime(2018, 11, 19, 22, 0): 1875, datetime.datetime(2018, 11, 19, 23, 0): 6, datetime.datetime(2018, 11, 20, 0, 0): 0, datetime.datetime(2018, 11, 20, 1, 0): 1, datetime.datetime(2018, 11, 20, 2, 0): 0, datetime.datetime(2018, 11, 20, 3, 0): 0, datetime.datetime(2018, 11, 20, 4, 0): 1, datetime.datetime(2018, 11, 20, 5, 0): 1, datetime.datetime(2018, 11, 20, 6, 0): 1, datetime.datetime(2018, 11, 20, 7, 0): 4}}製作圖表
粉絲牌等級情況 柱狀圖
def autolabel(ax, rects, xpos='center'):
xpos = xpos.lower()
ha = {'center': 'center', 'right': 'left', 'left': 'right'}
offset = {'center': 0.5, 'right': 0.57, 'left': 0.43}
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() * offset[xpos], 1.01 * height,
'{}'.format(height), ha=ha[xpos], va='bottom')
def sign_bar_wiht_leve(t1):
"""
粉絲牌等級情況柱狀圖
:return:
"""
mean = [round(x, 1) for x in t1['mean'].values()]
min = t1['min'].values()
max = t1['max'].values()
ind = np.arange(len(mean))
width = 0.35
fig, ax = plt.subplots()
rects_2 = ax.bar(ind, min, width / 2, color='IndianRed', label='最低等級')
rects_1 = ax.bar(ind + width / 2, mean, width / 2, color='SkyBlue', label='平均等級')
rects_3 = ax.bar(ind + width, max, width / 2, color='Black', label='最高等級')
ax.set_ylabel('等級')
ax.set_title('粉絲牌等級 (2018/11/19 19:04:18 - 2018/11/20 7:56:42)')
ax.set_xticks(ind)
ax.set_xticklabels(t1['mean'].keys())
ax.legend()
fig = plt.gcf()
fig.set_size_inches(30, 10.5)
autolabel(ax=ax, rects=rects_1, xpos="center")
autolabel(ax=ax, rects=rects_2, xpos="center")
autolabel(ax=ax, rects=rects_3, xpos="center")
fig.savefig("粉絲牌等級.jpg")
fig.show()
粉絲牌佔比 餅圖
def sign_pie_with_count(t2):
"""
粉絲牌佔比
:param t2:
:return:
"""
sizes = list(t2['sign_count'].values())[:5]
labels = list(t2['sign_count'].keys())[:5]
plt.figure(figsize=(8, 4)) # 調節圖形大小
explode = (0, 0, 0, 0) # 將某一塊分割出來，值越大分割出的間隙越大
patches, text1, text2 = plt.pie(sizes,
labels=labels,
autopct='%3.2f%%', # 數值保留固定小數位
shadow=False, # 無陰影設置
startangle=90, # 逆時針起始角度設置
pctdistance=0.8) # 數值距圓心半徑倍數距離
plt.axis('equal')
plt.legend()
plt.title('粉絲牌佔比 \n(2018/11/19 19:04:18 - 2018/11/20 7:56:42)')
plt.savefig("粉絲牌佔比.jpg")
plt.show()
用戶等級分佈 折線圖
def user_line_with_count(t4):
"""
用戶等級分佈折線圖
:param t4:
:return:
"""
t4 = sorted(t4['level_count'].items(), key=lambda d: d[0])
x = [i[0] for i in t4]
y = [i[1] for i in t4]
plt.figure(figsize=(8, 4))
plt.plot(x, y, "b--", linewidth=1)
# 設置數字標籤
for a, b in zip(x, y):
plt.text(a, b, b, ha='center', va='bottom', fontsize=10)
plt.xlabel("用戶等級")
plt.ylabel("數量")
plt.title("用戶等級情況\n(2018/11/19 19:04:18 - 2018/11/20 7:56:42)")
plt.savefig("用戶等級情況.jpg")
plt.show()
每個時間段用戶以及彈幕量 折線圖
def show_label(x, y, plt):
# 設置數字標籤
for a, b in zip(x, y):
plt.text(a, b, b, ha='center', va='bottom', fontsize=10)
def user_time(t5, t6):
"""
每個時間段用戶以及彈幕量
:param t5:
:param t6:
:return:
"""
t_5 = {k.strftime("%Y-%m-%d %H"): v for k, v in t5['user_count'].items()}
t_6 = {k.strftime("%Y-%m-%d %H"): v for k, v in t6['user_count'].items()}
x_1 = t_5.keys()
y_1 = t_5.values()
x_2 = t_6.keys()
y_2 = t_6.values()
plt.figure(figsize=(9, 5))
plt.plot(x_1, y_1, "o-", linewidth=1, label='彈幕數量')
plt.plot(x_2, y_2, "g--", linewidth=1, label='在線人數')
show_label(x_1, y_1, plt)
show_label(x_2, y_2, plt)
plt.xticks(rotation=30)
plt.legend()
plt.xlabel("小時")
plt.ylabel("人數")
plt.title("每個時間段用戶以及彈幕量\n(2018/11/19 19:04:18 - 2018/11/20 7:56:42)")
plt.savefig("每個時間段用戶以及彈幕量.jpg")
plt.show()
