Joe*_*oey 6 python dataframe pandas
我有一个包含 96 列的数据框：
df.to_csv('result.csv')
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
抱歉（从excel复制），标签和数据不一致。
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。
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
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)
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
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
你已经快到了。
问题是每个子数据框中的列名称都不同。因此，当 pandas 执行 操作时concat，它不仅仅将数据帧附加到底部，它还会扩展数据帧以具有具有正确名称的新列，然后附加行。
您可以通过重命名子数据框中的列来解决这个问题，例如
for sub_df in pieces:
sub_df.columns=range(12)
我认为，请注意 df2包含df8您想要的内容。由于某种原因，您df1只包含前 7 列，而不是 12 列。我暂时认为这是一个拼写错误。
产生完整的工作代码（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
您注意到示例中列的意外排序。这不会影响我的解决方案，但为了完整性我会解释它。
输出中的列采用所谓的“词典顺序”。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。它们的排序顺序看起来几乎正确，但不知何故却是 1, 10, 11 ... 19, 2, 20 等等。这是因为排序像字典一样逐个字母排序，但0to9位于前面a