[英]Pivot matrix to time-series - Python
I've got a dataframe with date as first column and time as the name of the other columns.我有一个 dataframe 日期作为第一列,时间作为其他列的名称。
Date日期 | 13:00 13:00 | 14:00 14:00 | 15:00 15:00 | 16:00 16:00 | ... ... |
---|---|---|---|---|---|
2022-01-01 2022-01-01 | B乙 | R R | M米 | M米 | ... ... |
2022-01-02 2022-01-02 | B乙 | B乙 | B乙 | M米 | ... ... |
2022-01-03 2022-01-03 | R R | B乙 | B乙 | M米 | ... ... |
How could I transform that matrix into a datetime time-series?如何将该矩阵转换为日期时间时间序列? My objective its something like this:我的目标是这样的:
Date日期 | Data数据 |
---|---|
2022-01-01 13:00 2022-01-01 13:00 | B乙 |
2022-01-01 14:00 2022-01-01 14:00 | R R |
2022-01-01 15:00 2022-01-01 15:00 | M米 |
2022-01-01 16:00 2022-01-01 16:00 | M米 |
... ... | ... ... |
I think it could be done using pivot.我认为可以使用 pivot 来完成。 I would really appreciate any help you could give me.我真的很感激你能给我的任何帮助。 Thanks in advance!!提前致谢!!
Try .set_index
/ .stack
.尝试.set_index
/ .stack
。 The rest is just convert the string to DateTime: rest 只是将字符串转换为日期时间:
df = df.set_index("Date").stack().reset_index()
df["Date"] = pd.to_datetime(df["Date"] + " " + df["level_1"])
df = df.rename(columns={0: "Data"})
print(df[["Date", "Data"]])
Prints:印刷:
Date Data
0 2022-01-01 13:00:00 B
1 2022-01-01 14:00:00 R
2 2022-01-01 15:00:00 M
3 2022-01-01 16:00:00 M
4 2022-01-02 13:00:00 B
5 2022-01-02 14:00:00 B
6 2022-01-02 15:00:00 B
7 2022-01-02 16:00:00 M
8 2022-01-03 13:00:00 R
9 2022-01-03 14:00:00 B
10 2022-01-03 15:00:00 B
11 2022-01-03 16:00:00 M
An alternative:替代:
df = pd.DataFrame({'Date': ['2022-01-01', '2022-01-02', '2022-01-03'], '13:00': ['B', 'B', 'R'], '14:00': ['R', 'B', 'R'], '15:00': ['M', 'B', 'B'], '16:00': ['M', 'M', 'M']})
df = df.melt(id_vars='Date', var_name='Time', value_name='Data')
df['Date'] = df['Date'] + ' ' + df['Time']
df = df[['Date', 'Data']]
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