flag_analysis/flag_videotime_viewcount.py at main · uky007/flag_analysis · GitHub

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import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import matplotlib.dates as mdates
import matplotlib.patches as mpatches
from matplotlib.path import Path
import matplotlib
from matplotlib.font_manager import FontProperties
from matplotlib import rcParams
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()


#データセット対象のCSV ファイル
file = sys.argv[1]
output_file = sys.argv[2]
#Date, 再生回数
num_play = pd.read_csv(file, header=0, encoding='UTF8', usecols=[3, 5], parse_dates=[0])
num_play = num_play.dropna(how='all')
#Date, 動画時間
num_time = pd.read_csv(file, header=0, encoding='UTF8', usecols=[3, 9], parse_dates=[0])
num_time = num_time.dropna(how='all')


# debug
#print(num_play)
#num_play.to_csv('exam_num_play.csv')

#Plot するグラフ
#Date
x = num_play[num_play.columns[0]]

#x = pd.date_range(x[0], x[218], freq='D')
#再生回数
y1 = num_play[num_play.columns[1]]

seconds = []
y9 = []
y10 = []

#video time

for i in range(len(x)):
    try:
        dt = datetime.datetime.strptime(num_time[num_time.columns[1]][i], "PT%MM%SS")
    except ValueError:
        try:
            dt = datetime.datetime.strptime(num_time[num_time.columns[1]][i], "PT%MM")
        except ValueError:
            dt = datetime.datetime.strptime(num_time[num_time.columns[1]][i], "PT%SS")

    y9.append(dt.time())
    sec = dt.minute * 60 + dt.second
    y10.append(sec)


vt_seconds = pd.Series(y10)
res = y1.corr(vt_seconds)
print("viewCount and videoTime corr:" + str(res))

#フォント設定
font_path = '/usr/share/fonts/truetype/takao-gothic/TakaoPGothic.ttf'
font_prop = FontProperties(fname=font_path)
matplotlib.rcParams['font.family'] = font_prop.get_name()

fig = plt.figure(figsize=(20.0, 12.0), dpi=300)
ax1 = fig.add_subplot()

#グラフの大きさ
#ax1.figure(figsize=(20.0, 10.0), dpi=300)

# グラフプロット
## 再生回数を棒グラフでプロット
play = ax1.bar(x, y1, width=1.0, bottom=None, color="lightblue")

ax2 = ax1.twinx()
time, = ax2.plot(x, y9, color='r')


# 軸の範囲
ax1.set_xlim('2019-11-01', x[len(x)-1])
ax1.set_ylim([0, 4000000])
ax2.set_ylim([0, '00:15:00'])

# グラフタイトル
ax1.set_title("全力回避フラグちゃん! 再生回数と動画時間", fontname="TakaoPGothic", fontsize=20)

# 軸メモリ
ax1.tick_params(axis='x', labelsize=20, labelrotation=45)
ax1.tick_params(axis='y', labelsize=20)
ax2.tick_params(axis='y', labelsize=20)

# 動画時間の合計
sum = vt_seconds.sum()
sum_h = int(sum / 3600)
sum_m = int((sum % 3600) / 60)
sum_s = int(sum % 60)
print("Sum of videoTime: " + str(sum_h) + ":" + str(sum_m).zfill(2) + ":" + str(sum_s).zfill(2))

#最大値，最小値
max = vt_seconds.max()
max_m = int(max / 60)
max_s = int(max % 60)
print("Max of videoTime: " + str(max_m) + ":" + str(max_s).zfill(2))
min = vt_seconds.min()
min_m = int(min / 60)
min_s = int(min % 60)
print("Min of videoTime: " + str(min_m) + ":" + str(min_s).zfill(2))


## 平均値と中央値を横線でプロット
avg = ax2.hlines(vt_seconds.mean(axis=0), x[0], x[len(x)-1], 'b', linestyles='dashed')
avg_m = int(vt_seconds.mean(axis=0) / 60)
avg_s = int(vt_seconds.mean(axis=0) % 60)
print("Average of videoTime: " + str(avg_m) + ":" + str(avg_s).zfill(2))
med = ax2.hlines(vt_seconds.median(axis=0), x[0], x[len(x)-1], 'r', linestyles='dashed')
med_m = int(vt_seconds.median(axis=0) / 60)
med_s = int(vt_seconds.median(axis=0) % 60)
print("Median of videoTime: " + str(med_m) + ":" + str(med_s).zfill(2))


# 分散
vt_var = np.var(y10)
vt_var_m = int(vt_var / 60)
vt_var_s = int(vt_var % 60)
print("Variance of videotime: " + str(vt_var))

# 標準偏差
vt_stdvar = np.std(y10)
vt_stdvar_m = int(vt_stdvar / 60)
vt_stdvar_s = int(vt_stdvar % 60)
print("Std. variance of videotime: " + str(vt_stdvar))

# ラベル名
ax1.set_xlabel("動画公開日", fontsize=20, fontname="TakaoPGothic")
ax1.set_ylabel("再生回数", fontname="TakaoPGothic", fontsize=20)
ax2.set_ylabel("動画時間", fontname="TakaoPGothic", fontsize=20, rotation=270, labelpad=30)

# 凡例
play_title = "再生回数"
vt_title = "動画時間"
avg_title = "平均値 (動画時間)"
med_title = "中央値 (動画時間)"

plt.legend([play, time, avg, med], [play_title, vt_title, avg_title, med_title], bbox_to_anchor=(1.0, 1.0), prop={"family":"TakaoPGothic", 'size':20}, markerscale=1)

#画像保存
plt.savefig(output_file, bbox_inches="tight", pad_inches=0.0)