Pandas 将多索引数据框转换为所有索引都在列中的简单数据框
我为这张照片道歉,但我不知道如何重现这张照片,因为我得到了这些数据 我只想将其转换为一个简单的数据帧,其中有索引列Pandas 将多索引数据框转换为所有索引都在列中的简单数据框,pandas,Pandas,我为这张照片道歉,但我不知道如何重现这张照片,因为我得到了这些数据 我只想将其转换为一个简单的数据帧,其中有索引列time,lon,lat,以及相应行中的值,如下所示: | time | lat | lon | data | 我尝试过这样做。reset_index(),但是时间轴仍然是横轴,而不是向下。如何“分解”所有索引值以获得一个简单的数据帧,其中所有索引都在列中 编辑: 用于复制的测试数据字典: {Timestamp('2001-01-01 00:00:00'): {(50.18000
time
,lon
,lat
,以及相应行中的值,如下所示:
| time | lat | lon | data |
我尝试过这样做。reset_index(),但是时间轴仍然是横轴,而不是向下。如何“分解”所有索引值以获得一个简单的数据帧,其中所有索引都在列中
编辑:
用于复制的测试数据字典:
{Timestamp('2001-01-01 00:00:00'): {(50.18000030517578,
-5.6199951171875): -1.68,
(50.18000030517578, -4.9200439453125): -1.88,
(50.18000030517578, -4.219970703125): -2.08},
Timestamp('2001-01-02 00:00:00'): {(50.18000030517578,
-5.6199951171875): -1.95,
(50.18000030517578, -4.9200439453125): -2.25,
(50.18000030517578, -4.219970703125): -2.55},
Timestamp('2001-01-03 00:00:00'): {(50.18000030517578,
-5.6199951171875): -0.76,
(50.18000030517578, -4.9200439453125): -0.91,
(50.18000030517578, -4.219970703125): -1.06},
Timestamp('2001-01-04 00:00:00'): {(50.18000030517578,
-5.6199951171875): -2.9,
(50.18000030517578, -4.9200439453125): -3.01,
(50.18000030517578, -4.219970703125): -3.11},
Timestamp('2001-01-05 00:00:00'): {(50.18000030517578,
-5.6199951171875): -2.06,
(50.18000030517578, -4.9200439453125): -2.29,
(50.18000030517578, -4.219970703125): -2.52}}
选中此项:
import pandas as pd
from pandas import Timestamp
d = {Timestamp('2001-01-01 00:00:00'): {(50.18000030517578,
-5.6199951171875): -1.68,
(50.18000030517578, -4.9200439453125): -1.88,
(50.18000030517578, -4.219970703125): -2.08},
Timestamp('2001-01-02 00:00:00'): {(50.18000030517578,
-5.6199951171875): -1.95,
(50.18000030517578, -4.9200439453125): -2.25,
(50.18000030517578, -4.219970703125): -2.55},
Timestamp('2001-01-03 00:00:00'): {(50.18000030517578,
-5.6199951171875): -0.76,
(50.18000030517578, -4.9200439453125): -0.91,
(50.18000030517578, -4.219970703125): -1.06},
Timestamp('2001-01-04 00:00:00'): {(50.18000030517578,
-5.6199951171875): -2.9,
(50.18000030517578, -4.9200439453125): -3.01,
(50.18000030517578, -4.219970703125): -3.11},
Timestamp('2001-01-05 00:00:00'): {(50.18000030517578,
-5.6199951171875): -2.06,
(50.18000030517578, -4.9200439453125): -2.29,
(50.18000030517578, -4.219970703125): -2.52}}
df = pd.DataFrame(d)
df = df.stack().to_frame().reset_index()
df.columns = ['lat', 'lon', 'time', 'data']
输出:
lat lon time data
0 50.18 -5.619995 2001-01-01 -1.68
1 50.18 -5.619995 2001-01-02 -1.95
2 50.18 -5.619995 2001-01-03 -0.76
3 50.18 -5.619995 2001-01-04 -2.90
4 50.18 -5.619995 2001-01-05 -2.06
5 50.18 -4.920044 2001-01-01 -1.88
6 50.18 -4.920044 2001-01-02 -2.25
7 50.18 -4.920044 2001-01-03 -0.91
8 50.18 -4.920044 2001-01-04 -3.01
9 50.18 -4.920044 2001-01-05 -2.29
10 50.18 -4.219971 2001-01-01 -2.08
11 50.18 -4.219971 2001-01-02 -2.55
12 50.18 -4.219971 2001-01-03 -1.06
13 50.18 -4.219971 2001-01-04 -3.11
14 50.18 -4.219971 2001-01-05 -2.52
选中此项:
import pandas as pd
from pandas import Timestamp
d = {Timestamp('2001-01-01 00:00:00'): {(50.18000030517578,
-5.6199951171875): -1.68,
(50.18000030517578, -4.9200439453125): -1.88,
(50.18000030517578, -4.219970703125): -2.08},
Timestamp('2001-01-02 00:00:00'): {(50.18000030517578,
-5.6199951171875): -1.95,
(50.18000030517578, -4.9200439453125): -2.25,
(50.18000030517578, -4.219970703125): -2.55},
Timestamp('2001-01-03 00:00:00'): {(50.18000030517578,
-5.6199951171875): -0.76,
(50.18000030517578, -4.9200439453125): -0.91,
(50.18000030517578, -4.219970703125): -1.06},
Timestamp('2001-01-04 00:00:00'): {(50.18000030517578,
-5.6199951171875): -2.9,
(50.18000030517578, -4.9200439453125): -3.01,
(50.18000030517578, -4.219970703125): -3.11},
Timestamp('2001-01-05 00:00:00'): {(50.18000030517578,
-5.6199951171875): -2.06,
(50.18000030517578, -4.9200439453125): -2.29,
(50.18000030517578, -4.219970703125): -2.52}}
df = pd.DataFrame(d)
df = df.stack().to_frame().reset_index()
df.columns = ['lat', 'lon', 'time', 'data']
输出:
lat lon time data
0 50.18 -5.619995 2001-01-01 -1.68
1 50.18 -5.619995 2001-01-02 -1.95
2 50.18 -5.619995 2001-01-03 -0.76
3 50.18 -5.619995 2001-01-04 -2.90
4 50.18 -5.619995 2001-01-05 -2.06
5 50.18 -4.920044 2001-01-01 -1.88
6 50.18 -4.920044 2001-01-02 -2.25
7 50.18 -4.920044 2001-01-03 -0.91
8 50.18 -4.920044 2001-01-04 -3.01
9 50.18 -4.920044 2001-01-05 -2.29
10 50.18 -4.219971 2001-01-01 -2.08
11 50.18 -4.219971 2001-01-02 -2.55
12 50.18 -4.219971 2001-01-03 -1.06
13 50.18 -4.219971 2001-01-04 -3.11
14 50.18 -4.219971 2001-01-05 -2.52
打印df.head(5).to_dict()
的结果,从这里我可以pd.DataFrame(…)
编辑-尽管我无法确定如何保存索引名(to_dict()自动删除它们(?)-感谢您提供的帮助。我添加了解决方案。打印df.head(5)的结果。从这里我可以pd.DataFrame()to_dict()
(…)
Edited-虽然我无法确定如何保留索引名(to_dict()自动删除它们(?)-感谢您提供的帮助。我添加了解决方案建议。.stack()
似乎是我想要的平展行索引!谢谢。有一件事需要改进:列名称处理,它应该更自动地完成;-).stack()
似乎是我想要的平展行索引!谢谢。有一件事需要改进:列名处理,它应该更自动地完成;-)