用python写代码展示转换与处理时序数据
时间: 2024-02-25 08:58:54 浏览: 69
Python的InfluxDB时序数据库读写代码
好的,以下是Python代码展示转换与处理时序数据:
```python
import pandas as pd
# 创建时序数据
time_series = pd.Series([1, 2, 3, 4], index=pd.date_range('20210101', periods=4, freq='D'))
# 显示时序数据
print(time_series)
# 将时序数据转换为DataFrame
df = time_series.to_frame()
# 显示转换后的DataFrame
print(df)
# 添加新列
df['B'] = [5, 6, 7, 8]
# 显示添加新列后的DataFrame
print(df)
# 将DataFrame转换为时序数据
new_time_series = df['A']
# 显示转换后的时序数据
print(new_time_series)
# 将时序数据重采样(Resample)
resampled_time_series = time_series.resample('2D').sum()
# 显示重采样后的时序数据
print(resampled_time_series)
# 将时序数据滑动窗口(Window)处理
windowed_time_series = time_series.rolling(window=2).sum()
# 显示滑动窗口处理后的时序数据
print(windowed_time_series)
# 将时序数据向前差分(Forward Difference)
diff_time_series = time_series.diff()
# 显示向前差分处理后的时序数据
print(diff_time_series)
# 将时序数据进行时序差分(Time Series Difference)
ts_diff_time_series = time_series.diff(periods=2)
# 显示时序差分处理后的时序数据
print(ts_diff_time_series)
# 将时序数据进行时序平滑(Time Series Smoothing)
smoothed_time_series = time_series.ewm(span=2).mean()
# 显示时序平滑处理后的时序数据
print(smoothed_time_series)
```
阅读全文