准备与可视化增量数据

用 Python 分析 IoT 数据

Matthias Voppichler

IT Developer

数据准备

透视数据
重采样
应用 diff()
应用 pct_change()

数据结构

print(data.head())

                timestamp device   value
0 2018-10-26 06:30:42.817   C331  6020.0
1 2018-10-26 06:30:43.083   M640   104.0
2 2018-10-26 06:31:00.858   M640   126.0
3 2018-10-26 06:31:10.254   C331  6068.0
4 2018-10-26 06:31:10.474   M640   136.0

透视表

解释透视表的图

应用透视表

                timestamp device   value
0 2018-10-26 06:30:42.817   C331  6020.0
1 2018-10-26 06:30:43.083   M640   104.0
2 2018-10-26 06:31:00.858   M640   126.0
3 2018-10-26 06:31:10.254   C331  6068.0
4 2018-10-26 06:31:10.474   M640   136.0

data = pd.pivot_table(data, columns="device", values="value", index="timestamp")
print(data.head()

device                     C331   M640
timestamp                             
2018-10-26 06:30:42.817  6020.0    NaN
2018-10-26 06:30:43.083     NaN  104.0
2018-10-26 06:31:00.858     NaN  126.0
2018-10-26 06:31:10.254  6068.0    NaN
2018-10-26 06:31:10.474     NaN  136.0

重采样

# 将 DataFrame 重采样为 1 分钟
df = data.resample("1min").max().dropna()
print(df.head())

device                 C331   M640
timestamp                         
2018-10-26 06:30:00  6020.0  104.0
2018-10-26 06:31:00  6129.0  180.0
2018-10-26 06:32:00  6205.0  256.0
2018-10-26 06:33:00  6336.0  332.0
2018-10-26 06:34:00  6431.0  402.0

可视化数据

data.plot()
plt.show()

pd.diff()

# 差分
df_diff = data.diff(1)
df_diff.plot()
plt.show()

数据分析——差分

# 差分
df_diff = data.diff()
df_diff.plot()
plt.show()

# 重采样后再做差分
df = data.resample('30min').max()
df_diff = df.diff()
df_diff.plot()
plt.show()

变化百分比

df_pct = df_diff.pct_change()
df_pct.plot()

Vamos praticar!

用 Python 分析 IoT 数据