如何在 Pandas 中将数据帧堆叠在一起
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原文地址: http://stackoverflow.com/questions/30840128/
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时间：2020-09-13 23:28:36 来源:igfitidea点击:
How to stack data frames on top of each other in Pandas
提问by Joey
I have a dataframe with 96 columns:
我有一个包含 96 列的数据框：
df.to_csv('result.csv')
out (excel):
出（优秀）：
Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9 Run 10 Run 11 Run 12 Run 13 Run 14 Run 15 Run 16 Run 17 Run 18 Run 19 Run 20 Run 21 Run 22 Run 23 Run 24 Run 25 Run 26 Run 27 Run 28 Run 29 Run 30 Run 31 Run 32 Run 33 Run 34 Run 35 Run 36 Run 37 Run 38 Run 39 Run 40 Run 41 Run 42 Run 43 Run 44 Run 45 Run 46 Run 47 Run 48 Run 49 Run 50 Run 51 Run 52 Run 53 Run 54 Run 55 Run 56 Run 57 Run 58 Run 59 Run 60 Run 61 Run 62 Run 63 Run 64 Run 65 Run 66 Run 67 Run 68 Run 69 Run 70 Run 71 Run 72 Run 73 Run 74 Run 75 Run 76 Run 77 Run 78 Run 79 Run 80 Run 81 Run 82 Run 83 Run 84 Run 85 Run 86 Run 87 Run 88 Run 89 Run 90 Run 91 Run 92 Run 93 Run 94 Run 95 Run 96
12 5194322.07 5195697.94 5195730.25 5196009.11 5195054.02 5193386.44 5192664.99 5193381.71 5193652.29 5193637.02 5191110.57 5190267.47 5190739.45 5190416.85 5189592.97 5188898.89 5188461.01 5188735.44 5189156.83 5188870.35 5188306.12 5187746.51 5188023.45 5187536.91 5188085.85 5188634.95 5187861.42 5188124.97 5187076.75 5189218.62 5189052.51 5188571.63 5188486.76 5188502.68 5188318.63 5188512.5 5188409.83 5188250.86 5188885.18 5188999.83 5189365.09 5190159.72 5189771.1 5190136.3 5191179.72 5191256.35 5191147.97 5191712.32 5192430.88 5193407.95 5192603.89 5192248.7 5192197.65 5193096.79 5193005.25 5193985.8 5193451.22 5193489.16 5193562.72 5194621.43 5194170.84 5194198.19 5194866.16 5194030.81 5194421.67 5193745.31 5195458.37 5196342.62 5194881.29 5195036.46 5193627.87 5194470.9 5195017.44 5194402.87 5194659.24 5194751.51 5195016.87 5194802.11 5195467.68 5194654.04 5195622.23 5194709.45 5195050.77 5195097.58 5195987.22 5195831.3 5194776.48 5193605.12 5194317.87 5194089.21 5194563.64 5193895.14 5194140.95 5193791.85 5193915.21 5194343.34
13 1453304.43 1454792.33 1454807.96 1454768.09 1455077.49 1454644.59 1454545.94 1454930.93 1455214.85 1455342.12 1455188.92 1454972.08 1455358.05 1455533.45 1455208.56 1454913.89 1455124.45 1454644.83 1455071.25 1454812.46 1454838.9 1454842.33 1454895.52 1454838.55 1454888.25 1455024.08 1454624.57 1455159.29 1454889.65 1454906.92 1454789.36 1454579.06 1455060.12 1455108.26 1455289.8 1455269.54 1455227.93 1455734.55 1455846 1455774.16 1456130.24 1456289.94 1455711.1 1456447.68 1456588.78 1456796.61 1456867.04 1457081.75 1457274.68 1457155.16 1457782.73 1457065.11 1457459.15 1457347.08 1457837.54 1457999.87 1458171.82 1458241.76 1458320.08 1458622.22 1458574.79 1458586.67 1458701.91 1458749.17 1458869.01 1458755.66 1458885 1459167.12 1458881.12 1459110.4 1458918 1459297.49 1459375.28 1459338.09 1459413.22 1459726.96 1459926.75 1459943.81 1460193.37 1460242.02 1460274.7 1460319.25 1460494.5 1460347.8 1460589.02 1460436.82 1460754.06 1460643.79 1460803.29 1460817.97 1460948.1 1460903.97 1460944.45 1460874.04 1460929.33 1461072.84
14 193379.75 193027.34 192806.25 192501.2 192602.7 192477.86 192402.72 192408.76 192421.74 192400.59 192345.37 192312.98 192331.79 192357.29 192277.84 192270.06 192232.67 192170.09 192216.06 192182.3 192163.13 192145.32 192164.63 192157.59 192134.08 192172.82 192098.36 192146.81 192106.65 192082.12 192057.73 192065.45 192080.46 192128.27 192096.82 192120.97 192081.97 192166.45 192157.38 192121.78 192203.97 192215.73 192098.89 192181.45 192211.12 192234.93 192245.5 192282.35 192290.05 192278.03 192370.19 192250.39 192308.68 192264.65 192339.55 192365.62 192394.12 192385.72 192403.42 192431.52 192408.2 192414.77 192419.74 192424.98 192432.85 192422.79 192444.94 192454.58 192456.89 192449 192451.98 192507.83 192490.77 192504.55 192520.85 192539.33 192549.03 192578.96 192618.21 192638.83 192629.15 192617.57 192651.62 192626.81 192649.6 192636.68 192703.22 192661.42 192687.33 192704.48 192729.77 192731.6 192742.22 192701.82 192729.55 192743.99
15 157553.12 157252.95 157080.77 156941.24 156887.86 156776.95 156669.69 156664.82 156695.03 156652.3 156653.55 156576.01 156586.19 156620.33 156558.26 156539 156501.76 156445.28 156465.98 156435.93 156436.62 156444.92 156422.48 156446.25 156426.4 156447.14 156397.15 156421.85 156391.94 156370.94 156337.46 156364.73 156380.6 156399.43 156389.75 156386.21 156346.02 156453.62 156442.94 156392.71 156436.89 156449.56 156363.24 156443.5 156448.72 156429.21 156479.57 156498 156535.45 156528.7 156603.81 156486.44 156524.97 156482.7 156558.25 156574.41 156585.45 156572.05 156610.77 156638.11 156607.3 156600.19 156626.51 156605.06 156637.24 156611.04 156625.38 156635.17 156644.67 156634.81 156635.81 156690.93 156666.67 156700.49 156702.44 156705.8 156723.19 156746.24 156784.77 156783.37 156816.63 156767.56 156820.49 156805.69 156799.75 156813.77 156847.63 156837.49 156841.06 156833.21 156873.62 156877.83 156877.8 156836.4 156876.97 156889.26
16 98894.09 98661.73 98517.97 98463.94 98381.17 98335.16 98248.41 98271.91 98279.43 98235.13 98240.75 98182.86 98200.03 98201.4 98172.12 98146.85 98131.82 98103.18 98111.26 98070.4 98089.39 98103.34 98063.18 98087.61 98055.12 98101.77 98064.3 98073.7 98044.23 98032.22 98024.03 98035.75 98047.34 98065.01 98070.35 98056.62 98025.54 98091.08 98101.41 98052.04 98079.79 98094.76 98012.52 98088.28 98083.11 98091.65 98097.78 98111.77 98133.52 98135.26 98181.02 98130.98 98142.39 98103.2 98151.5 98163.1 98181.58 98161.11 98181.91 98207.14 98176.71 98194 98203.63 98178.89 98213.34 98179.43 98188.91 98209 98224.92 98202.98 98199.12 98239.48 98228.15 98251.45 98263.44 98253.43 98253.53 98293.22 98310.56 98299.46 98324.44 98304.57 98320.92 98331.45 98315.52 98316.35 98350.96 98356.69 98336.74 98322.17 98356.82 98367.58 98355.18 98342.84 98346.7 98374.95
17
12 3129.52 3147.16 3160.49 3171.33 3214.77 3236.69 3275 3280.86 3287.46 3302.41 3331.16 3375.36 3371.95 3378.69 3377.02 3373.65 3397.39 3388.79 3416.8 3457.74 3447.79 3456.51 3455.32 3495.66 3492.27 3505.96 3510.87 3533.18 3522.81 3524.65 3572.99 3575.17 3581.11 3579.16 3584.39 3601.6 3591.13 3619.1 3581.45 3597.28 3610.98 3627.1 3641.58 3639.8 3628.65 3655.72 3649.4 3648.33 3676.89 3661.96 3697.21 3689.05 3693.71 3710.29 3734.39 3732.68 3732.57 3760.45 3753.94 3778.77 3792.45 3764.17 3804.36 3804.46 3807.49 3817.46 3854.72 3820.18 3844.23 3844.19 3856.38 3856.53 3856.95 3905.39 3868.66 3898.41 3908.94 3905.1 3942.19 3941.65 3957.9 3936.05 3953.8 3952.5 3986.09 3972.33 3974.86 3962.69 4006.6 4007.01 4013.11 4036.94 3981.75 3982.69 3982.16 4017.66
13 2946817.59 2944662.04 2941123.24 2940256.46 2935558.38 2931746.01 2928978.21 2928741.48 2926931.72 2926556.07 2924090.79 2923586 2923616.24 2921712.71 2921325.33 2921606.4 2921049.04 2920501.43 2920219.82 2919483.39 2919055.94 2918261.89 2917710.17 2918281.08 2917460.36 2918447.78 2917467.81 2917025.06 2914725.09 2917582.26 2916970.03 2917224.28 2917123.34 2916758.73 2916377.49 2916374.34 2915134.44 2916170.14 2916194.3 2916438.93 2916841.46 2916923.27 2916298.61 2916843.73 2917128.84 2916505.26 2917823.72 2917249.33 2918275.17 2918657.92 2918593.87 2918092.17 2916450.26 2917383.96 2917260.84 2918251.2 2916669.62 2917421.6 2916740.6 2917259.97 2916818.62 2917508.43 2918006.44 2917757.21 2918060.88 2915855.65 2918151.83 2917179.65 2918271.42 2917682.08 2916528.9 2916751.82 2916524.81 2916802.18 2915576.36 2916073.58 2916285.37 2915885.03 2916843.4 2916897 2916226.64 2916329.2 2915110.13 2914458.88 2916433.5 2915075.24 2915229.63 2913743.19 2914563.32 2913637.85 2914569.65 2914736.45 2913404.01 2913008.04 2913627.33 2914075.7
14 230608.01 230312.12 229832.18 229627.99 229210.49 228632.32 228363.82 228331.31 228218.46 228144.81 227964.12 227953.21 228026.95 227840.86 227741.13 227753.98 227740.91 227540.28 227557.01 227318.88 227512.95 227296.66 227285.16 227307.5 227240.68 227426.04 227221.35 227178.03 226972.82 227183.08 227053.52 227103.26 227226.78 227095.99 227295.09 227357.08 226998.07 227155.45 227099.14 227256.82 227092.64 227274.71 227046.17 227211.35 227271.17 227113.9 227296.57 227410.79 227169.76 227314.2 227496.21 227252.71 227267.55 227308.58 227361.06 227333.64 227178.12 227358.18 227154.37 227278.39 227226.46 227220.65 227276.21 227336.72 227239.63 227201.77 227298.1 227286.38 227398.71 227336.91 227365.81 227255.2 227241.09 227129.99 227074.28 227152.61 227331.13 227349.15 227404.26 227317.63 227228.69 227163.85 226953.01 226922.52 227207.38 227141.31 227117.31 227162.19 227210.19 227078.05 227066.16 227226.41 226951.75 226963.37 226956.1 227106.71
15 25607.66 25705.26 25483.12 25478.82 25410.26 25384.14 25296.8 25297.31 25185.21 25310.15 25275.49 25246.13 25249.32 25322.94 25258.49 25231.09 25294.81 25282.72 25211.84 25373.49 25201.84 25277.95 25356.21 25331.92 25191.69 25268.33 25359.69 25177.89 25275.49 25293.96 25212.5 25256.51 25209.77 25207.68 25245.21 25130.97 25246.49 25073.61 25141.74 25191.55 25275.35 25218.04 25234.66 25144.64 25294.39 25197.99 25252.66 25029.12 25239.01 25241.82 25299.99 25249.92 25112.44 25140.67 25247.73 25235.76 25316.81 25188.8 25122.93 25186.04 25149.75 25127.59 25175.98 25093.1 25224.16 25166.84 25170.27 25203.5 25258.65 25192.54 25154.19 25180.35 25197.78 25340.6 25224.43 25173.91 25205.17 25253.24 25325.32 25459.4 25186.6 25238.33 25237.85 25130.99 25303.22 25188.74 25226.41 25190.27 25082.58 25059.87 25295.12 25197.11 25222.02 25208.2 25173.89 25197.88
sorry (copied from excel), the labels and data are not in line.
抱歉（从excel复制），标签和数据不一致。
Basically, I want to seperate this dataframe into 8 units... so for the first unit, Unit 1, I want 12 columns.... runs 1 to 12. and so on. Unit 8 will be run 85 - 96.
基本上，我想把这个数据帧分成 8 个单元......所以对于第一个单元，Unit 1，我想要 12 列......运行 1 到 12。等等。第 8 单元将运行 85 - 96。
I just renamed 8 dataframes and specified the columns I wanted to take from the main dataframe (df)
我刚刚重命名了 8 个数据框并指定了我想从主数据框 (df) 中获取的列
Here is my code for this:
这是我的代码：
df1 = df.ix[:,0:7]
df2 = df.ix[:,12:24]
df3 = df.ix[:,24:36]
df4 = df.ix[:,36:48]
df5 = df.ix[:,48:60]
df6 = df.ix[:,60:72]
df7 = df.ix[:,72:84]
df8 = df.ix[:,84:96]
pieces = (df1,df2,df3,df4,df5,df6,df7,df8)
Finally, I used concat to concatenate the 8 dataframes together (Stacked on top of each other). However, the output it somewhat distorted and it is not in order.
最后，我使用 concat 将 8 个数据帧连接在一起（堆叠在彼此之上）。然而，它的输出有点失真，而且不正常。
df_final = pd.concat(pieces, ignore_index = True)
print df_final
out:
出去：
Run 1 Run 13 Run 14 Run 15 Run 16 Run 17 Run 18 Run 19 Run 2 Run 20 Run 21 Run 22 Run 23 Run 24 Run 25 Run 26 Run 27 Run 28 Run 29 Run 3 Run 30 Run 31 Run 32 Run 33 Run 34 Run 35 Run 36 Run 37 Run 38 Run 39 Run 4 Run 40 Run 41 Run 42 Run 43 Run 44 Run 45 Run 46 Run 47 Run 48 Run 49 Run 5 Run 50 Run 51 Run 52 Run 53 Run 54 Run 55 Run 56 Run 57 Run 58 Run 59 Run 6 Run 60 Run 61 Run 62 Run 63 Run 64 Run 65 Run 66 Run 67 Run 68 Run 69 Run 7 Run 70 Run 71 Run 72 Run 73 Run 74 Run 75 Run 76 Run 77 Run 78 Run 79 Run 80 Run 81 Run 82 Run 83 Run 84 Run 85 Run 86 Run 87 Run 88 Run 89 Run 90 Run 91 Run 92 Run 93 Run 94 Run 95 Run 96
0 5194322.07 5195697.94 5195730.25 5196009.11 5195054.02 5193386.44 5192664.99
1 1453304.43 1454792.33 1454807.96 1454768.09 1455077.49 1454644.59 1454545.94
2 193379.75 193027.34 192806.25 192501.2 192602.7 192477.86 192402.72
3 157553.12 157252.95 157080.77 156941.24 156887.86 156776.95 156669.69
4 98894.09 98661.73 98517.97 98463.94 98381.17 98335.16 98248.41
5
6 3129.52 3147.16 3160.49 3171.33 3214.77 3236.69 3275
7 2946817.59 2944662.04 2941123.24 2940256.46 2935558.38 2931746.01 2928978.21
8 230608.01 230312.12 229832.18 229627.99 229210.49 228632.32 228363.82
9 25607.66 25705.26 25483.12 25478.82 25410.26 25384.14 25296.8
10 5190739.45 5190416.85 5189592.97 5188898.89 5188461.01 5188735.44 5189156.83 5188870.35 5188306.12 5187746.51 5188023.45 5187536.91
11 1455358.05 1455533.45 1455208.56 1454913.89 1455124.45 1454644.83 1455071.25 1454812.46 1454838.9 1454842.33 1454895.52 1454838.55
12 192331.79 192357.29 192277.84 192270.06 192232.67 192170.09 192216.06 192182.3 192163.13 192145.32 192164.63 192157.59
13 156586.19 156620.33 156558.26 156539 156501.76 156445.28 156465.98 156435.93 156436.62 156444.92 156422.48 156446.25
14 98200.03 98201.4 98172.12 98146.85 98131.82 98103.18 98111.26 98070.4 98089.39 98103.34 98063.18 98087.61
15
16 3371.95 3378.69 3377.02 3373.65 3397.39 3388.79 3416.8 3457.74 3447.79 3456.51 3455.32 3495.66
17 2923616.24 2921712.71 2921325.33 2921606.4 2921049.04 2920501.43 2920219.82 2919483.39 2919055.94 2918261.89 2917710.17 2918281.08
18 228026.95 227840.86 227741.13 227753.98 227740.91 227540.28 227557.01 227318.88 227512.95 227296.66 227285.16 227307.5
19 25249.32 25322.94 25258.49 25231.09 25294.81 25282.72 25211.84 25373.49 25201.84 25277.95 25356.21 25331.92
20 5188085.85 5188634.95 5187861.42 5188124.97 5187076.75 5189218.62 5189052.51 5188571.63 5188486.76 5188502.68 5188318.63 5188512.5
21 1454888.25 1455024.08 1454624.57 1455159.29 1454889.65 1454906.92 1454789.36 1454579.06 1455060.12 1455108.26 1455289.8 1455269.54
22 192134.08 192172.82 192098.36 192146.81 192106.65 192082.12 192057.73 192065.45 192080.46 192128.27 192096.82 192120.97
23 156426.4 156447.14 156397.15 156421.85 156391.94 156370.94 156337.46 156364.73 156380.6 156399.43 156389.75 156386.21
24 98055.12 98101.77 98064.3 98073.7 98044.23 98032.22 98024.03 98035.75 98047.34 98065.01 98070.35 98056.62
25
26 3492.27 3505.96 3510.87 3533.18 3522.81 3524.65 3572.99 3575.17 3581.11 3579.16 3584.39 3601.6
27 2917460.36 2918447.78 2917467.81 2917025.06 2914725.09 2917582.26 2916970.03 2917224.28 2917123.34 2916758.73 2916377.49 2916374.34
28 227240.68 227426.04 227221.35 227178.03 226972.82 227183.08 227053.52 227103.26 227226.78 227095.99 227295.09 227357.08
29 25191.69 25268.33 25359.69 25177.89 25275.49 25293.96 25212.5 25256.51 25209.77 25207.68 25245.21 25130.97
30 5188409.83 5188250.86 5188885.18 5188999.83 5189365.09 5190159.72 5189771.1 5190136.3 5191179.72 5191256.35 5191147.97 5191712.32
31 1455227.93 1455734.55 1455846 1455774.16 1456130.24 1456289.94 1455711.1 1456447.68 1456588.78 1456796.61 1456867.04 1457081.75
32 192081.97 192166.45 192157.38 192121.78 192203.97 192215.73 192098.89 192181.45 192211.12 192234.93 192245.5 192282.35
33 156346.02 156453.62 156442.94 156392.71 156436.89 156449.56 156363.24 156443.5 156448.72 156429.21 156479.57 156498
34 98025.54 98091.08 98101.41 98052.04 98079.79 98094.76 98012.52 98088.28 98083.11 98091.65 98097.78 98111.77
35
36 3591.13 3619.1 3581.45 3597.28 3610.98 3627.1 3641.58 3639.8 3628.65 3655.72 3649.4 3648.33
37 2915134.44 2916170.14 2916194.3 2916438.93 2916841.46 2916923.27 2916298.61 2916843.73 2917128.84 2916505.26 2917823.72 2917249.33
38 226998.07 227155.45 227099.14 227256.82 227092.64 227274.71 227046.17 227211.35 227271.17 227113.9 227296.57 227410.79
39 25246.49 25073.61 25141.74 25191.55 25275.35 25218.04 25234.66 25144.64 25294.39 25197.99 25252.66 25029.12
40 5192430.88 5193407.95 5192603.89 5192248.7 5192197.65 5193096.79 5193005.25 5193985.8 5193451.22 5193489.16 5193562.72 5194621.43
41 1457274.68 1457155.16 1457782.73 1457065.11 1457459.15 1457347.08 1457837.54 1457999.87 1458171.82 1458241.76 1458320.08 1458622.22
42 192290.05 192278.03 192370.19 192250.39 192308.68 192264.65 192339.55 192365.62 192394.12 192385.72 192403.42 192431.52
43 156535.45 156528.7 156603.81 156486.44 156524.97 156482.7 156558.25 156574.41 156585.45 156572.05 156610.77 156638.11
44 98133.52 98135.26 98181.02 98130.98 98142.39 98103.2 98151.5 98163.1 98181.58 98161.11 98181.91 98207.14
45
46 3676.89 3661.96 3697.21 3689.05 3693.71 3710.29 3734.39 3732.68 3732.57 3760.45 3753.94 3778.77
47 2918275.17 2918657.92 2918593.87 2918092.17 2916450.26 2917383.96 2917260.84 2918251.2 2916669.62 2917421.6 2916740.6 2917259.97
48 227169.76 227314.2 227496.21 227252.71 227267.55 227308.58 227361.06 227333.64 227178.12 227358.18 227154.37 227278.39
49 25239.01 25241.82 25299.99 25249.92 25112.44 25140.67 25247.73 25235.76 25316.81 25188.8 25122.93 25186.04
50 5194170.84 5194198.19 5194866.16 5194030.81 5194421.67 5193745.31 5195458.37 5196342.62 5194881.29 5195036.46 5193627.87 5194470.9
51 1458574.79 1458586.67 1458701.91 1458749.17 1458869.01 1458755.66 1458885 1459167.12 1458881.12 1459110.4 1458918 1459297.49
52 192408.2 192414.77 192419.74 192424.98 192432.85 192422.79 192444.94 192454.58 192456.89 192449 192451.98 192507.83
53 156607.3 156600.19 156626.51 156605.06 156637.24 156611.04 156625.38 156635.17 156644.67 156634.81 156635.81 156690.93
54 98176.71 98194 98203.63 98178.89 98213.34 98179.43 98188.91 98209 98224.92 98202.98 98199.12 98239.48
55
56 3792.45 3764.17 3804.36 3804.46 3807.49 3817.46 3854.72 3820.18 3844.23 3844.19 3856.38 3856.53
57 2916818.62 2917508.43 2918006.44 2917757.21 2918060.88 2915855.65 2918151.83 2917179.65 2918271.42 2917682.08 2916528.9 2916751.82
58 227226.46 227220.65 227276.21 227336.72 227239.63 227201.77 227298.1 227286.38 227398.71 227336.91 227365.81 227255.2
59 25149.75 25127.59 25175.98 25093.1 25224.16 25166.84 25170.27 25203.5 25258.65 25192.54 25154.19 25180.35
60 5195017.44 5194402.87 5194659.24 5194751.51 5195016.87 5194802.11 5195467.68 5194654.04 5195622.23 5194709.45 5195050.77 5195097.58
61 1459375.28 1459338.09 1459413.22 1459726.96 1459926.75 1459943.81 1460193.37 1460242.02 1460274.7 1460319.25 1460494.5 1460347.8
62 192490.77 192504.55 192520.85 192539.33 192549.03 192578.96 192618.21 192638.83 192629.15 192617.57 192651.62 192626.81
63 156666.67 156700.49 156702.44 156705.8 156723.19 156746.24 156784.77 156783.37 156816.63 156767.56 156820.49 156805.69
64 98228.15 98251.45 98263.44 98253.43 98253.53 98293.22 98310.56 98299.46 98324.44 98304.57 98320.92 98331.45
65
66 3856.95 3905.39 3868.66 3898.41 3908.94 3905.1 3942.19 3941.65 3957.9 3936.05 3953.8 3952.5
67 2916524.81 2916802.18 2915576.36 2916073.58 2916285.37 2915885.03 2916843.4 2916897 2916226.64 2916329.2 2915110.13 2914458.88
68 227241.09 227129.99 227074.28 227152.61 227331.13 227349.15 227404.26 227317.63 227228.69 227163.85 226953.01 226922.52
69 25197.78 25340.6 25224.43 25173.91 25205.17 25253.24 25325.32 25459.4 25186.6 25238.33 25237.85 25130.99
70 5195987.22 5195831.3 5194776.48 5193605.12 5194317.87 5194089.21 5194563.64 5193895.14 5194140.95 5193791.85 5193915.21 5194343.34
71 1460589.02 1460436.82 1460754.06 1460643.79 1460803.29 1460817.97 1460948.1 1460903.97 1460944.45 1460874.04 1460929.33 1461072.84
72 192649.6 192636.68 192703.22 192661.42 192687.33 192704.48 192729.77 192731.6 192742.22 192701.82 192729.55 192743.99
73 156799.75 156813.77 156847.63 156837.49 156841.06 156833.21 156873.62 156877.83 156877.8 156836.4 156876.97 156889.26
74 98315.52 98316.35 98350.96 98356.69 98336.74 98322.17 98356.82 98367.58 98355.18 98342.84 98346.7 98374.95
75
76 3986.09 3972.33 3974.86 3962.69 4006.6 4007.01 4013.11 4036.94 3981.75 3982.69 3982.16 4017.66
77 2916433.5 2915075.24 2915229.63 2913743.19 2914563.32 2913637.85 2914569.65 2914736.45 2913404.01 2913008.04 2913627.33 2914075.7
78 227207.38 227141.31 227117.31 227162.19 227210.19 227078.05 227066.16 227226.41 226951.75 226963.37 226956.1 227106.71
79 25303.22 25188.74 25226.41 25190.27 25082.58 25059.87 25295.12 25197.11 25222.02 25208.2 25173.89 25197.88
This is my problem. I want the dataframes in line and stacked on top of each other.
这是我的问题。我希望数据框排成一行并堆叠在一起。
采纳答案by J Richard Snape
You're very nearly there.
你快到了。
The problem is that the column names are all different within each sub dataframe. Thus, when pandas does the concat, it doesn't just append the dataframes to the bottom, it expands the dataframe to have new colums with the right names and then appends the rows.
问题是每个子数据框中的列名都不同。因此，当 Pandas 执行 时concat，它不只是将数据帧附加到底部，而是扩展数据帧以获得具有正确名称的新列，然后附加行。
You can solve this by renaming the columns in the sub dataframes e.g.
您可以通过重命名子数据框中的列来解决此问题，例如
for sub_df in pieces:
sub_df.columns=range(12)
N.B.df2to df8contain what you want, I think. For some reason, you've made df1contain only the first 7 columns, rather than 12. I'll assume that's a typo for now.
NBdf2到df8包含你想要什么，我想。出于某种原因，您df1只包含前 7 列，而不是 12 列。我现在假设这是一个错字。
Resulting in full working code (I copied your input data into a file named 'data1.csv')
生成完整的工作代码（我将您的输入数据复制到名为 的文件中'data1.csv'）
import pandas as pd
import numpy as np
df = pd.read_csv('data1.csv')
df1 = df.ix[:,0:12]
df2 = df.ix[:,12:24]
df3 = df.ix[:,24:36]
df4 = df.ix[:,36:48]
df5 = df.ix[:,48:60]
df6 = df.ix[:,60:72]
df7 = df.ix[:,72:84]
df8 = df.ix[:,84:96]
pieces = (df1,df2,df3,df4,df5,df6,df7,df8)
# Give the columns the same labels in each sub dataframe
# I've used numbers for convenience - you can give more descriptive names if you want
for sub_df in pieces:
sub_df.columns=range(12)
df_final = pd.concat(pieces, ignore_index = True)
print df_final
Final note on ordering
关于订购的最后说明
You note the unexpected ordering of your columns in your example. This won't affect my solution, but I will explain it for completeness.
您注意到示例中列的意外排序。这不会影响我的解决方案，但我会解释它的完整性。
The columns in your output are in what is called 'Lexicographic ordering'. This is a common problem when sorting strings containing numbers in Python (and other languages). They are sorted in an order that looks almost right, but somehow runs 1, 10, 11 ... 19, 2, 20 and so on. That is because the ordering sorts letter by letter like a dictionary, but with 0to 9coming before a
输出中的列是所谓的'Lexicographic ordering'。这是在 Python（和其他语言）中对包含数字的字符串进行排序时的常见问题。它们以看起来几乎正确的顺序排序，但不知何故运行 1, 10, 11 ... 19, 2, 20 等等。这是因为通过排序像字典一样的字母排序的信，但0要9来临前a
