如何绘制每日最大值
How to plot the daily maximum values
我正在尝试绘制数据框列 (ext_temp) 的每天最大值：
import pandas as pd
data = {'vin': {0: 'VF1AG0000KF908155', 1: 'VF1AG0000KF908155', 2: 'VF1AG0000KF908155', 3: 'VF1AG0000KF908155', 4: 'VF1AG0000KF908155', 5: 'VF1AG0000KF908155', 6: 'VF1AG0000KF908155', 7: 'VF1AG0000KF908155', 8: 'VF1AG0000KF908155', 9: 'VF1AG0000KF908155'}, 'date': {0: pd.Timestamp('2019-09-27 07:07:02'), 1: pd.Timestamp('2019-09-27 09:23:08'), 2: pd.Timestamp('2019-09-27 09:39:08'), 3: pd.Timestamp('2020-07-15 11:46:41'), 4: pd.Timestamp('2020-07-16 07:17:52'), 5: pd.Timestamp('2020-07-16 09:23:47'), 6: pd.Timestamp('2020-09-11 07:43:05'), 7: pd.Timestamp('2020-09-17 15:00:33'), 8: pd.Timestamp('2020-10-21 06:49:58'), 9: pd.Timestamp('2020-10-21 14:47:33')}, 'sohe': {0: 101, 1: 101, 2: 101, 3: 96, 4: 96, 5: 96, 6: 96, 7: 96, 8: 96, 9: 96}, 'soc': {0: 60, 1: 63, 2: 99, 3: 66, 4: 68, 5: 69, 6: 86, 7: 58, 8: 9, 9: 9}, 'ext_temp': {0: 27, 1: 30, 2: 31, 3: 30, 4: 26, 5: 29, 6: 26, 7: 29, 8: 28, 9: 27}, 'battery_temp': {0: 27, 1: 33, 2: 32, 3: 26, 4: 26, 5: 26, 6: 26, 7: 30, 8: 27, 9: 29}}
df = pd.DataFrame(data)
不幸的是，当尝试使用
nd = "VF1AG0000KF908155"
df = charge[charge.vin==gop]
df = df.groupby(pd.Grouper(key = 'date', freq = 'D'))
fig,ax = plt.subplots()
ax.plot(df.date, df['ext_temp'].max())
我收到以下错误消息：
VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
- 使用
pd.Grouper 将用 NaN 填补缺失的天数
- 如果您不想填写缺失的日期，请使用
.dt 提取器对 'date' 的日期部分进行分组。
- 使用
pandas.DataFrame.plot绘制数据框
使用了kind='bar'，因为数据不多。对于线图，使用 kind='line'.
pd.Grouper
- 注意需要使用
.dropna()，至少要绘制条形图。
dfg = df.groupby(pd.Grouper(key='date', freq='D'))['ext_temp'].max().dropna()
ax = dfg.plot(kind='bar')
dfg = df.groupby(pd.Grouper(key='date', freq='D'))['ext_temp'].max().dropna()
ax = dfg.plot(kind='line')
.dt.date
- Groupby 仅
'date' 列的 date 组件
dfg = df.groupby(df.date.dt.date)['ext_temp'].max()
ax = dfg.plot(kind='bar')
我正在尝试绘制数据框列 (ext_temp) 的每天最大值：
import pandas as pd
data = {'vin': {0: 'VF1AG0000KF908155', 1: 'VF1AG0000KF908155', 2: 'VF1AG0000KF908155', 3: 'VF1AG0000KF908155', 4: 'VF1AG0000KF908155', 5: 'VF1AG0000KF908155', 6: 'VF1AG0000KF908155', 7: 'VF1AG0000KF908155', 8: 'VF1AG0000KF908155', 9: 'VF1AG0000KF908155'}, 'date': {0: pd.Timestamp('2019-09-27 07:07:02'), 1: pd.Timestamp('2019-09-27 09:23:08'), 2: pd.Timestamp('2019-09-27 09:39:08'), 3: pd.Timestamp('2020-07-15 11:46:41'), 4: pd.Timestamp('2020-07-16 07:17:52'), 5: pd.Timestamp('2020-07-16 09:23:47'), 6: pd.Timestamp('2020-09-11 07:43:05'), 7: pd.Timestamp('2020-09-17 15:00:33'), 8: pd.Timestamp('2020-10-21 06:49:58'), 9: pd.Timestamp('2020-10-21 14:47:33')}, 'sohe': {0: 101, 1: 101, 2: 101, 3: 96, 4: 96, 5: 96, 6: 96, 7: 96, 8: 96, 9: 96}, 'soc': {0: 60, 1: 63, 2: 99, 3: 66, 4: 68, 5: 69, 6: 86, 7: 58, 8: 9, 9: 9}, 'ext_temp': {0: 27, 1: 30, 2: 31, 3: 30, 4: 26, 5: 29, 6: 26, 7: 29, 8: 28, 9: 27}, 'battery_temp': {0: 27, 1: 33, 2: 32, 3: 26, 4: 26, 5: 26, 6: 26, 7: 30, 8: 27, 9: 29}}
df = pd.DataFrame(data)
不幸的是，当尝试使用
nd = "VF1AG0000KF908155"
df = charge[charge.vin==gop]
df = df.groupby(pd.Grouper(key = 'date', freq = 'D'))
fig,ax = plt.subplots()
ax.plot(df.date, df['ext_temp'].max())
我收到以下错误消息：
VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
- 使用
pd.Grouper将用NaN 填补缺失的天数
- 如果您不想填写缺失的日期，请使用
.dt提取器对'date'的日期部分进行分组。 - 使用
pandas.DataFrame.plot绘制数据框-
使用了
kind='bar'，因为数据不多。对于线图，使用kind='line'.
pd.Grouper
- 注意需要使用
.dropna()，至少要绘制条形图。
dfg = df.groupby(pd.Grouper(key='date', freq='D'))['ext_temp'].max().dropna()
ax = dfg.plot(kind='bar')
dfg = df.groupby(pd.Grouper(key='date', freq='D'))['ext_temp'].max().dropna()
ax = dfg.plot(kind='line')
.dt.date
- Groupby 仅
'date'列的date组件
dfg = df.groupby(df.date.dt.date)['ext_temp'].max()
ax = dfg.plot(kind='bar')