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active_report.py
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active_report.py
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import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
class ActivityReport:
def __init__(self, org):
self.org = org
# Read the data file
self.active_csv = pd.read_csv(f'data/{org}/{org}_activity_details.csv')
# 首先排除'Last Activity Date'列为空的行
self.active_csv = self.active_csv.dropna(subset=['Last Activity Date'])
# Filter the data for the last `days` days
# recent_activity = recent_activity.dropna(subset=['Last Activity Date'])
self.active_csv['Last Activity Date'] = self.active_csv['Last Activity Date'].apply(lambda x: x.split('+')[0])
self.active_csv['Last Activity Date'] = pd.to_datetime(self.active_csv['Last Activity Date']) # Convert to timestamp
# print(self.active_csv)
# 写一个方法，分析data/{self.org}_last_activity.csv，显示最近30天，按照IDE统计的使用频率排序，并画图（饼状图）；
def print_ide_usage(self, days=30, column='IDE', type='pie'):
# print(data)
end_date = pd.Timestamp.now()
start_date = end_date - pd.Timedelta(days=days)
data = self.active_csv[(self.active_csv['Last Activity Date'] >= start_date)]
# Count the number of times each IDE was used
column_counts = data[column].value_counts()
# Sort the IDEs by usage frequency
column_counts = column_counts.sort_values(ascending=False)
# print(column_counts)
# print(column_counts.index)
# print(column_counts.values)
# print(list(column_counts.index))
# return None
# 如果column_counts太长，会导致饼图显示不全，所以需要限制一下，只显示前10个内容
if len(column_counts) > 10:
column_counts = column_counts.head(10)
# print(column_counts)
matplotlib.use('Agg')
# Plot the results as a pie chart or a bar chart based on the type parameter
if type.upper() == 'PIE':
plt.pie(column_counts.values, labels=column_counts.index, autopct='%1.0f%%')
plt.title(f'{column} Usage in the Last {days} Days')
# 在每个饼图上显示具体值
for i, v in enumerate(column_counts.values):
plt.text(i, v, f"{v}", color='blue', fontsize=12, ha='center', va='center')
plt.savefig(f'static/{self.org}/{self.org}_{column}_Pie.png')
#plt.show()
elif type.upper() == 'BAR':
labels = list(column_counts.index)
plt.bar(labels, column_counts.values)
plt.title(f'{column} Usage in the Last {days} Days')
plt.xlabel(f'{column}')
plt.ylabel('Number of Uses')
plt.xticks(rotation=15)
# 在每个柱子上显示具体值
for i, v in enumerate(column_counts.values):
plt.text(i, v, f"{v}", color='blue', fontsize=12, ha='center', va='bottom')
plt.savefig(f'static/{self.org}/{self.org}_{column}_Bar.png')
#plt.show()
elif type.upper() == 'REPORT':
print(f"Index: {column_counts.index}")
print(f"Values: {column_counts.values}")
# 每次都是产生一个新的figure，如果不清除，会产生多个figure
# 先判断下是否有figure存在，如果有，则清除
if plt.fignum_exists(1):
plt.clf()
# 写一个方法，分析data/{self.org}_last_activity.csv，显示最近30天，按天按人统计的每天日活用户数；
# 首先针对Last Activity Date 列，需要分解出来Day，因为我们需要按照Day来统计，所以需要把Day单独拿出来，然后再统计
# 拿出来的方法是：data['Last Activity Date'].apply(lambda x: x.split('T')[0])
# Last Activity Date列的内容样例是，2023-09-04T16:56:59+08:00，分解出来的时间是2023-09-04
# 然后再把这个时间转换成时间戳，data['Last Activity Date'] = pd.to_datetime(data['Last Activity Date'])
#然后按照login,Last Activity Date分组，然后统计每组的数量，data.groupby(['login','Last Activity Date']).size()
# 然后再按照Last Activity Date分组，统计每组的数量，data.groupby(['Last Activity Date']).size()
def print_daily_active_users(self, days=30):
"""
Prints the daily active users for the last `days` days and saves a scatter plot of the results.
Args:
- days (int): The number of days to include in the report. Default is 30.
Returns:
- None
"""
data = pd.read_csv(f'data/{self.org}/{self.org}_activity_details.csv')
# 首先排除'Last Activity Date'列为空的行
data = data.dropna(subset=['Last Activity Date'])
# Filter the data for the last `days` days
end_date = pd.Timestamp.now()
start_date = end_date - pd.Timedelta(days=days)
data['Last Activity Date'] = data['Last Activity Date'].apply(lambda x: x.split('T')[0])
data['Last Activity Date'] = pd.to_datetime(data['Last Activity Date'])
data = data[(data['Last Activity Date'] >= start_date)]
# print(data)
# 按照login,Last Activity Date分组，然后统计每组的数量，并按照Last Activity Date排列
data = data.groupby(['Login','Last Activity Date']).size().reset_index(name='counts').sort_values(by='Last Activity Date')
# print(data)
# 按照Last Activity Date分组，统计每组的数量
data = data.groupby(['Last Activity Date']).size().reset_index(name='counts')
# print(data)
# Plot the results as a scatter plot
plt.plot(data['Last Activity Date'], data['counts'], '-o')
plt.title(f'Daily Active Users in Last {days} Days')
plt.xlabel('Date')
plt.ylabel('Number of Daily Active Users')
plt.xticks(rotation=45)
plt.yticks(range(0, int(max(data['counts']))+10, 5))
plt.gca().yaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
plt.savefig(f'static/{self.org}/{self.org}_active_users_byday.png')
#plt.show()
# 清空图片
plt.clf()