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I have a dataframe with 96 columns:
df.to_csv('result.csv')
out (excel):
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.
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.
I just renamed 8 dataframes and specified the columns I wanted to take from the main dataframe (df)
Here is my code for this:
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.
df_final = pd.concat(pieces, ignore_index = True)
print df_final
out:
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.
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When we concatenate DataFrames, we need to specify the axis. axis=0 tells pandas to stack the second DataFrame UNDER the first one. It will automatically detect whether the column names are the same and will stack accordingly. axis=1 will stack the columns in the second DataFrame to the RIGHT of the first DataFrame.
Using rbind() to merge two R data frames This function stacks the two data frames on top of each other, appending the second data frame to the first. For this function to operate, both data frames need to have the same number of columns and the same column names.
Pandas merge() function is used to merge multiple Dataframes. We can use either pandas. merge() or DataFrame. merge() to merge multiple Dataframes.
Another way to combine DataFrames is to use columns in each dataset that contain common values (a common unique id). Combining DataFrames using a common field is called “joining”. The columns containing the common values are called “join key(s)”.
You're very nearly there.
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.
You can solve this by renaming the columns in the sub dataframes e.g.
for sub_df in pieces:
sub_df.columns=range(12)
N.B. df2 to df8 contain what you want, I think. For some reason, you've made df1 contain only the first 7 columns, rather than 12. I'll assume that's a typo for now.
Resulting in full working code (I copied your input data into a file named '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
You note the unexpected ordering of your columns in your example. This won't affect my solution, but I will explain it for completeness.
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 0 to 9 coming before a
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