本文整理汇总了Python中pandas.groupby方法的典型用法代码示例。如果您正苦于以下问题：Python pandas.groupby方法的具体用法？Python pandas.groupby怎么用？Python pandas.groupby使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pandas的用法示例。
在下文中一共展示了pandas.groupby方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _plotWeekdayStats
# 需要导入模块: import pandas [as 别名]
# 或者: from pandas import groupby [as 别名]
def _plotWeekdayStats(stats, columns, groupBy=True):
dataToPlot = stats.copy()
# Group by weekday and rename date column
if groupBy:
dataToPlot = dataToPlot.groupby(stats['date'].dt.weekday).mean()
dataToPlot = dataToPlot.reset_index().rename(columns={'date':'weekday'})
# change stats from columns to row attribute
dataToPlot = pd.melt(dataToPlot, id_vars=['weekday'], value_vars=columns,
var_name='stats', value_name='val')
# Rename stats and weekdays
dataToPlot['stats'].replace(NAMES, inplace=True)
dataToPlot['weekday'].replace(dayOfWeek, inplace=True)
# Plot
g = sns.factorplot(data=dataToPlot, x="weekday", y="val", col="stats",
order=dayOfWeekOrder, kind="point", sharey=False, col_wrap=3)
g.set_xticklabels(rotation=45)
g.set(xlabel='')
return g
#sns.plt.show() 示例2: test_groupby
# 需要导入模块: import pandas [as 别名]
# 或者: from pandas import groupby [as 别名]
def test_groupby(self):
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
pd.groupby(pd.Series([1, 2, 3]), [1, 1, 1]) 示例3: _prepareWeekdayByMonthStats
# 需要导入模块: import pandas [as 别名]
# 或者: from pandas import groupby [as 别名]
def _prepareWeekdayByMonthStats(stats):
# Add day and month columns, and groupby
stats = stats.copy()
stats['day'] = stats['date'].dt.weekday
stats['month'] = stats['date'].dt.month
dataToPlot = stats.groupby(['day', 'month']).mean()
dataToPlot = dataToPlot.reset_index()
dataToPlot['day'].replace(dayOfWeek, inplace=True)
dataToPlot['month'].replace(months, inplace=True)
return dataToPlot
# def plotWeekdayStats(stats, columns):
# """
# Plot aggregated (mean) stats by dayOfWeek
# :param stats: data to plot
# :param columns: columns from stats to plot
# """
# MEASURE_NAME = 'weekday'
# dayOfWeek={0:'Mon', 1:'Tue', 2:'Wed', 3:'Thur', 4:'Fri', 5:'Sat', 6:'Sun'}
# order = ['Mon','Tue','Wed','Thur','Fri','Sat','Sun']
# stats[MEASURE_NAME] = stats[MEASURE_NAME].map(dayOfWeek)
#
# f, axes = getAxes(2,2)
# for i, c in enumerate(columns):
# if c in NAMES:
# c = NAMES[c]
# g = sns.barplot(x=MEASURE_NAME, y=c, data=stats, order=order, ax=axes[i])
# g.set_xlabel('')
# sns.plt.show()
# #plot(stats, columns, MEASURE_NAME, 2, 3, order=order) 示例4: _prepareYearAndMonthStats
# 需要导入模块: import pandas [as 别名]
# 或者: from pandas import groupby [as 别名]
def _prepareYearAndMonthStats(stats, columns):
# Group by month and change stats from columns to row attribute
dataToPlot = stats.groupby(stats['date'].dt.to_period("M")).mean()
dataToPlot = pd.melt(dataToPlot.reset_index(), id_vars=['date'], value_vars=columns,
var_name='stats', value_name='val')
# Rename stats
dataToPlot['stats'].replace(NAMES, inplace=True)
return dataToPlot 示例5: plotDailyStatsHb
# 需要导入模块: import pandas [as 别名]
# 或者: from pandas import groupby [as 别名]
def plotDailyStatsHb(data):
ax = data.groupby(data[hbStats.NAME_DT_COL].dt.date).mean().plot()
#data.groupby(data[hbStats.NAME_DT_COL].dt.date).mean().rolling(30).mean().plot(ax=ax)
sns.plt.show() 示例6: _plotMonthlyStats
# 需要导入模块: import pandas [as 别名]
# 或者: from pandas import groupby [as 别名]
def _plotMonthlyStats(stats, columns, groupBy=True):
dataToPlot = stats.copy()
# Group by month and rename date column
if groupBy:
dataToPlot = dataToPlot.groupby(stats['date'].dt.month).mean()
dataToPlot = dataToPlot.reset_index().rename(columns={'date': 'month'})
# change stats from columns to row attribute
dataToPlot = pd.melt(dataToPlot, id_vars=['month'], value_vars=columns,
var_name='stats', value_name='val')
# Rename stats and weekdays
dataToPlot['stats'].replace(NAMES, inplace=True)
dataToPlot['month'].replace(months, inplace=True)
order = [m for m in monthsOrder if m in dataToPlot['month'].unique()]
# Plot
g = sns.factorplot(data=dataToPlot, x="month", y="val", col="stats", order=order, kind="bar", sharey=False)
g.set_xticklabels(rotation=45)
g.set(xlabel='')
return g
#sns.plt.show()
# def _plotMonthlyStats(stats, columns):
# """
# Plot aggregated (mean) stats by month
# :param stats: data to plot
# :param columns: columns from stats to plot
# """
# MEASURE_NAME = 'month'
# months={1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug',
# 9:'Sep', 10:'Oct', 11:'Nov', 12:'Dec'}
# order = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
# stats[MEASURE_NAME] = stats[MEASURE_NAME].map(months)
#
# order = [m for m in order if m in stats[MEASURE_NAME].unique()]
#
# f, axes = getAxes(2,2)
# for i, c in enumerate(columns):
# if c in NAMES:
# c = NAMES[c]
# g = sns.barplot(x=MEASURE_NAME, y=c, data=stats, order=order, ax=axes[i])
# g.set_xlabel('')
# sns.plt.show()