如何在除了日期列的所有行都为NaN的地方dropna? [英] How to dropna where all rows are NaN except the date column?
本文介绍了如何在除了日期列的所有行都为NaN的地方dropna?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试从csv文件中删除NaN值，但我只想删除所有列均为空的行.我要删除的行的图片附在下面.
链接到文件:
基本上，如果B，C，D，E，F，G，H列为NaN，则删除整个行
我尝试使用下面的代码，但它删除了所有内容
将pandas导入为pddf = pd.read_csv("testing.csv")df = df.dropna(阈值= 7) 最终结果将如下所示
数据
<代码>，打开，高，低，关闭，调整关闭，交易量，新加坡2015-10-01,2795.399902,3104.719971,2765.439941,2998.350098,2998.350098,0.0，2015-11-01,2976.719971,3043.850098,2843.949951,2855.939941,2855.939941,0.0，2015-12-01,2862.790039,2911.439941,2793.389893,2882.72998,2882.72998,0.0，2016-01-01,2889.22998,2890.209961,2529.01001,2629.110107,2629.110107,0.0，2016-02-01,2637.050049,2684.790039,2528.439941,2666.51001,2666.51001,0.0，2016-03-01,2666.709961,2906.800049,2654.97998,2840.899902,2840.899902,0.0，2016-04-01,2820.659912,2964.100098,2783.419922,2838.52002,2838.52002,158708700.0，2016-05-01,2842.860107,2848.899902,2713.469971,2791.060059,2791.060059,0.0，2016-06-01,2787.98999,2881.919922,2703.47998,2840.929932,2840.929932,0.0，2016-07-01,2848.449951,2958.899902,2830.0,2868.689941,2868.689941,0.0，2016-08-01,2875.590088,2898.27002,2810.8798829999996,2820.590088,2820.590088,0.0，2016-09-01,2821.929932,2911.840088,2791.3798829999996,2869.469971,2869.469971,0.0，2016-10-01,2879.850098,2901.72998,2783.330078,2813.8701170000004,2813.8701170000004,0.0，2016-11-01,2814.080078,2915.419922,2760.969971,2905.169922,2905.169922,0.0，2016-12-01,2913.649902,2980.77002,2857.909912,2880.76001,2880.76001,0.0，2017-01-01,2887.0,3065.1298829999996,2869.659912,3046.800049,3046.800049,0.0，2017-02-01,3045.939941,3138.969971,3030.649902,3096.610107,3096.610107,4018227800.0，2017-03-01,3106.300049,3188.02002,3104.330078,3175.110107,3175.110107,5462555700.0，2017-04-01,3180.27002,3189.810059,3113.899902,3175.439941,3175.439941,4292226700.0，2017-05-01,3183.429932,3275.389893,3183.409912,3210.820068,3210.820068,5080433500.0，2017-06-01,3214.1201170000004,3270.919922,3196.48999,3226.47998,3226.47998,4414015100.0，2017-07-01,3228.909912,3354.709961,3196.139893,3329.52002,3329.52002,5085548600.0，2017-08-01,3321.5,3349.090088,3244.22998,3277.26001,3277.26001,4856835500.0，2017-09-01,3274.389893,3275.139893,3193.409912,3219.909912,3219.909912,3840282400.0，2017-10-01,3233.949951,3392.149902,3230.810059,3374.080078,3374.080078,4261116400.0，2017-11-01,3377.1899409999996,3449.320068,3341.300049,3433.540039,3433.540039,4789747800.0，2017-12-01,3441.850098,3469.360107,3370.219971,3402.919922,3402.919922,3386126700.0，2018-01-01,3406.4799799999996,3611.6899409999996,3403.8701170000004,3533.98999,3533.98999,4727173600.0，2018-02-01,3536.929932,3574.5900880000004,3340.550049,3517.9399409999996,3517.9399409999996,6143735500.0，2018-03-01,3493.4399409999996,3555.9799799999996,3382.780029,3427.969971,3427.969971,4963081900.0，2018-04-01,3439.040039,3628.429932,3338.959961,3613.929932,3613.929932,4599803900.0，2018-05-01,3624.1999509999996,3641.649902,3428.179932,3428.179932,3428.179932,5918362800.0，2018-06-01,3423.5,3492.3400880000004,3237.77002,3268.699951,3268.699951,5500961400.0，2018-07-01,3277.429932,3341.419922,3176.26001,3319.850098,3319.850098,5029346600.0，2018-08-01,3331.050049,3347.97998,3187.830078,3213.47998,3213.47998,5005791600.0，2018-09-01,3209.969971,3265.01001,3102.72998,3257.050049,3257.050049,4158150600.0，2018-10-01,3262.429932,3272.8798829999996,2955.679932,3018.800049,3018.800049,5516696000.0，2018-11-01,3045.679932,3132.419922,3007.310059,3117.610107,3117.610107,4457632700.0，2018-12-01,3154.219971,3192.8798829999996,3000.449951,3068.76001,3068.76001,3627597800.0，2019-01-01,3072.98999,3250.27002,2993.419922,3190.169922,3190.169922,4467841200.0，2019-02-01,3194.219971,3286.080078,3174.0,3212.689941,3212.689941,3786000800.0，2019-03-01,3210.840088,3251.719971,3156.790039,3212.8798829999996,3212.8798829999996,4128594600.0，2019-04-01,3229.110107,3415.179932,3227.6201170000004,3400.1999509999996,3400.1999509999996,4447727600.0，2019-05-01,3389.5200200000004,3397.179932,3110.51001,3117.76001,3117.76001,4319537800.0，2019-06-01,3111.51001,3336.080078,3104.030029,3321.610107,3321.610107,4160448600.0，2019-07-01,3339.580078,3386.649902,3299.889893,3300.75,3300.75,4489792100.0，2019-08-01,3282.790039,3311.26001,3040.159912,3106.52002,3106.52002,5146051500.0，2019-09-01,3092.25,3216.8701170000004,3074.040039,3119.98999,3119.98999,4116898900.0，2019-10-01,3130.110107,3235.23999,3068.830078,3229.8798829999996,3229.8798829999996,4402690200.0，2019-11-01,3227.600098,3285.719971,3182.050049,3193.919922,3193.919922,7055882400.0，2019-12-01,3198.27002,3239.23999,3144.070068,3222.830078,3222.830078,4536740600.0，2020-01-01,3230.47998,3283.889893,3144.100098,3153.72998,3153.72998,4951167700.0，2020-02-01,3131.02002,3233.860107,3008.459961,3011.080078,3011.080078,5320489700.0，2020-02-21 、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、、等44.02020-02-25 、、、、、、2020-02-28 、、、、、、、 22.02020-03-01,2988.350098,3047.790039,2208.419922,2481.22998,2481.22998,7767702900.0，2020-03-02 ,,,,,,,2020-03-03 ,,,,,,,2020-03-06 、、、、、、、、 23.02020-03-10 、、、、、、2020-03-13 、、、、、、、 21.02020-03-17 ,,,,,,,2020-03-20 ,,,,,, 24.02020-03-23 、、、、、、2020-03-24 ,,,,,,,2020-03-27 、、、、、、、、 27.02020-03-302020-03-31 、、、、、、、2020-04-01,2468.169922,2671.580078,2380.840088,2624.22998,2624.22998,7238328000.0，2020-04-03 ,,,,,, 37.02020-04-06 、、、、、、、2020-04-07 、、、、、、、2020-04-10 ,,,,,, 73.02020-04-13 、、、、、、2020-04-14 、、、、、、、2020-04-17 ,,,,,,, 85.02020-04-20 、、、、、、、2020-04-21 、、、、、、、2020-04-24 ,,,,,,, 90.02020-04-27 、、、、、、2020-04-28 、、、、、、2020-05-01,2555.669922,2611.73999,2489.939941,2510.75,2510.75,7367276100.0,90.02020-05-05 、、、、、、、2020-05-15 、、、、、、、2020-05-21 、、、、、、、2020-05-22 、、、、、、、 92.02020-05-25 、、、、、、2020-05-26 、、、、、、、2020-05-30 、、、、、、、2020-06-01,2519.419922,2839.389893,2516.459961,2589.909912,2589.909912,8396435700.0，2020-06-05 ,,,,,, 89.02020-06-08 、、、、、、、2020-06-15 、、、、、、、2020-06-16 、、、、、、2020-06-19 、、、、、、、 92.02020-06-22 、、、、、、、2020-06-25 、、、、、、2020-07-01,2604.080078,2707.669922,2511.02002,2529.820068,2529.820068,4876221500.0，2020-07-03 ,,,,,,,2020-07-06 、、、、、、、2020-07-07 ,,,,,,, 90.02020-07-12 、、、、、、2020-07-14 、、、、、、、2020-07-20 、、、、、、、 92.02020-07-26 ,,,,,,,2020-07-27 、、、、、、、2020-07-31 、、、、、、、2020-08-01,2522.530029,2602.330078,2478.389893,2532.51001,2532.51001,6347053700.0，2020-08-03 ,,,,,, 88.02020-08-072020-08-102020-08-12 、、、、、、2020-08-14 ,,,,,,, 90.02020-08-172020-08-25 、、、、、、2020-08-28 ,,,,,, 90.02020-08-31 、、、、、、、2020-09-01,2521.810059,2546.8701170000004,2476.820068,2490.090088,2490.090088,2000718800.0，2020-09-11,2481.080078,2492.419922,2476.820068,2490.090088,2490.090088,0.0，
解决方案
- 使用
pandas.read_csv，将parse_dates和index_col设置为索引0处的未命名日期列. -
.dropna和how ='all'，这将删除所有完全为NaN的行.不考虑索引，这就是为什么将date列设置为索引的原因. - 从技术上讲，日期不必解析为日期时间，但这是财务数据，因此对于时间序列分析，它应采用正确的日期时间格式，因为它将正确绘制.日期列必须是以这种方式轻松地
.dropna的索引.
df = pd.read_csv('testing.csv'，parse_dates = [0]，index_col = 0)#drop nadf = df.dropna(how ='all')# 保存存档df.to_csv('test_updated.csv'，index = True)#显示(df)开盘高位低位关闭成交量新加坡2015-10-01 2795.39990 3104.71997 2765.43994 2998.35010 2998.35010 0.00000e + 00 NaN2015-11-01 2976.71997 3043.85010 2843.94995 2855.93994 2855.93994 0.00000e + 00 NaN2015-12-01 2862.79004 2911.43994 2793.38989 2882.72998 2882.72998 0.00000e + 00 NaN2016-01-01 2889.22998 2890.20996 2529.01001 2629.11011 2629.11011 0.00000e + 00 NaN2016-02-01 2637.05005 2684.79004 2528.43994 2666.51001 2666.51001 0.00000e + 00 NaN2016-03-01 2666.70996 2906.80005 2654.97998 2840.89990 2840.89990 0.00000e + 00 NaN2016-04-01 2820.65991 2964.10010 2783.41992 2838.52002 2838.52002 1.58709e + 08 NaN2016-05-01 2842.86011 2848.89990 2713.46997 2791.06006 2791.06006 0.00000e + 00 NaN2016-06-01 2787.98999 2881.91992 2703.47998 2840.92993 2840.92993 0.00000e + 00 NaN2016-07-01 2848.44995 2958.89990 2830.00000 2868.68994 2868.68994 0.00000e + 00 NaN2016-08-01 2875.59009 2898.27002 2810.87988 2820.59009 2820.59009 0.00000e + 00 NaN2016-09-01 2821.92993 2911.84009 2791.37988 2869.46997 2869.46997 0.00000e + 00 NaN2016-10-01 2879.85010 2901.72998 2783.33008 2813.87012 2813.87012 0.00000e + 00 NaN2016-11-01 2814.08008 2915.41992 2760.96997 2905.16992 2905.16992 0.00000e + 00 NaN2016-12-01 2913.64990 2980.77002 2857.90991 2880.76001 2880.76001 0.00000e + 00 NaN2017-01-01 2887.00000 3065.12988 2869.65991 3046.80005 3046.80005 0.00000e + 00 NaN2017-02-01 3045.93994 3138.96997 3030.64990 3096.61011 3096.61011 4.01823e + 09 NaN2017-03-01 3106.30005 3188.02002 3104.33008 3175.11011 3175.11011 5.46256e + 09 NaN2017-04-01 3180.27002 3189.81006 3113.89990 3175.43994 3175.43994 4.29223e + 09 NaN2017-05-01 3183.42993 3275.38989 3183.40991 3210.82007 3210.82007 5.08043e + 09 NaN2017-06-01 3214.12012 3270.91992 3196.48999 3226.47998 3226.47998 4.41402e + 09 NaN2017-07-01 3228.90991 3354.70996 3196.13989 3329.52002 3329.52002 5.08555e + 09 NaN2017-08-01 3321.50000 3349.09009 3244.22998 3277.26001 3277.26001 4.85684e + 09 NaN2017-09-01 3274.38989 3275.13989 3193.40991 3219.90991 3219.90991 3.84028e + 09 NaN2017-10-01 3233.94995 3392.14990 3230.81006 3374.08008 3374.08008 4.26112e + 09 NaN2017-11-01 3377.18994 3449.32007 3341.30005 3433.54004 3433.54004 4.78975e + 09 NaN2017-12-01 3441.85010 3469.36011 3370.21997 3402.91992 3402.91992 3.38613e + 09 NaN2018-01-01 3406.47998 3611.68994 3403.87012 3533.98999 3533.98999 4.72717e + 09 NaN2018-02-01 3536.92993 3574.59009 3340.55005 3517.93994 3517.93994 6.14374e + 09 NaN2018-03-01 3493.43994 3555.97998 3382.78003 3427.96997 3427.96997 4.96308e + 09 NaN2018-04-01 3439.04004 3628.42993 3338.95996 3613.92993 3613.92993 4.59980e + 09 NaN2018-05-01 3624.19995 3641.64990 3428.17993 3428.17993 3428.17993 5.91836e + 09 NaN2018-06-01 3423.50000 3492.34009 3237.77002 3268.69995 3268.69995 5.50096e + 09 NaN2018-07-01 3277.42993 3341.41992 3176.26001 3319.85010 3319.85010 5.02935e + 09 NaN2018-08-01 3331.05005 3347.97998 3187.83008 3213.47998 3213.47998 5.00579e + 09 NaN2018-09-01 3209.96997 3265.01001 3102.72998 3257.05005 3257.05005 4.15815e + 09 NaN2018-10-01 3262.42993 3272.87988 2955.67993 3018.80005 3018.80005 5.51670e + 09 NaN2018-11-01 3045.67993 3132.41992 3007.31006 3117.61011 3117.61011 4.45763e + 09 NaN2018-12-01 3154.21997 3192.87988 3000.44995 3068.76001 3068.76001 3.62760e + 09 NaN2019-01-01 3072.98999 3250.27002 2993.41992 3190.16992 3190.16992 4.46784e + 09 NaN2019-02-01 3194.21997 3286.08008 3174.00000 3212.68994 3212.68994 3.78600e + 09 NaN2019-03-01 3210.84009 3251.71997 3156.79004 3212.87988 3212.87988 4.12859e + 09 NaN2019-04-01 3229.11011 3415.17993 3227.62012 3400.19995 3400.19995 4.44773e + 09 NaN2019-05-01 3389.52002 3397.17993 3110.51001 3117.76001 3117.76001 4.31954e + 09 NaN2019-06-01 3111.51001 3336.08008 3104.03003 3321.61011 3321.61011 4.16045e + 09 NaN2019-07-01 3339.58008 3386.64990 3299.88989 3300.75000 3300.75000 4.48979e + 09 NaN2019-08-01 3282.79004 3311.26001 3040.15991 3106.52002 3106.52002 5.14605e + 09 NaN2019-09-01 3092.25000 3216.87012 3074.04004 3119.98999 3119.98999 4.11690e + 09 NaN2019-10-01 3130.11011 3235.23999 3068.83008 3229.87988 3229.87988 4.40269e + 09 NaN2019-11-01 3227.60010 3285.71997 3182.05005 3193.91992 3193.91992 7.05588e + 09 NaN2019-12-01 3198.27002 3239.23999 3144.07007 3222.83008 3222.83008 4.53674e + 09 NaN2020-01-01 3230.47998 3283.88989 3144.10010 3153.72998 3153.72998 4.95117e + 09 NaN2020-02-01 3131.02002 3233.86011 3008.45996 3011.08008 3011.08008 5.32049e + 09 NaN2020-02-21 NaN NaN NaN NaN NaN NaN NaN 24.02020-02-28 NaN NaN NaN NaN NaN NaN NaN 22.02020-03-01 2988.35010 3047.79004 2208.41992 2481.22998 2481.22998 7.76770e + 09 NaN2020-03-06 NaN NaN NaN NaN NaN NaN NaN 23.02020-03-13 NaN NaN NaN NaN NaN NaN NaN 21.02020-03-20 NaN NaN NaN NaN NaN NaN NaN 24.02020-03-27 NaN NaN NaN NaN NaN NaN NaN 27.02020-04-01 2468.16992 2671.58008 2380.84009 2624.22998 2624.22998 7.23833e + 09 NaN2020-04-03 NaN NaN NaN NaN NaN NaN NaN 37.02020-04-10 NaN NaN NaN NaN NaN NaN NaN 73.02020-04-17 NaN NaN NaN NaN NaN NaN NaN 85.02020-04-24 NaN NaN NaN NaN NaN NaN NaN 90.02020-05-01 2555.66992 2611.73999 2489.93994 2510.75000 2510.75000 7.36728e + 09 90.02020-05-22 NaN NaN NaN NaN NaN NaN NaN 92.02020-06-01 2519.41992 2839.38989 2516.45996 2589.90991 2589.90991 8.39644e + 09 NaN2020-06-05 NaN NaN NaN NaN NaN NaN NaN 89.02020-06-19 NaN NaN NaN NaN NaN NaN NaN 92.02020-07-01 2604.08008 2707.66992 2511.02002 2529.82007 2529.82007 4.87622e + 09 NaN2020-07-07 NaN NaN NaN NaN NaN NaN NaN 90.02020-07-20 NaN NaN NaN NaN NaN NaN NaN 92.02020-08-01 2522.53003 2602.33008 2478.38989 2532.51001 2532.51001 6.34705e + 09 NaN2020-08-03 NaN NaN NaN NaN NaN NaN NaN 88.02020-08-14 NaN NaN NaN NaN NaN NaN NaN 90.02020-08-28 NaN NaN NaN NaN NaN NaN NaN 90.02020-09-01 2521.81006 2546.87012 2476.82007 2490.09009 2490.09009 2.00072e + 09 NaN2020-09-11 2481.08008 2492.41992 2476.82007 2490.09009 2490.09009 0.00000e + 00 NaN 绘图
- 此绘图使用
pandas.DataFrame.plot，它使用matplotlib作为默认绘图引擎- 请注意，这并不是在NaN值之间绘制线，因此添加了
dropna进行绘制.
- 请注意，这并不是在NaN值之间绘制线，因此添加了
- 不要用值绘制
Volume，因为比例(y值)要大得多. -
'Singapore'是单独绘制的，因为它的值较低且数据点很少，因此它看起来像是条线图.
将matplotlib.pyplot导入为plt无花果，(ax1，ax2)= plt.subplots(nrows = 2，figsize =(9，10))df [['Open'，'High'，'Low'，'Close'，'Adj Close']].dropna().plot(ax = ax1)ax2.scatter(df.index，'Singapore'，data = df，label ='Singapore')ax2.legend()plt.show()
I am trying to remove NaN values from my csv file but I only want to remove the row where all columns are empty. A picture of the rows I want to remove is attached below.
Link to the file: https://filebin.net/ou93iqiinss02l0g
Essentially if column B,C,D,E,F,G,H is NaN, I remove the whole row
I tried using the below code but it removes all everything
import pandas as pd
df = pd.read_csv("testing.csv")
df = df.dropna(thresh = 7)
the end result will look like this
Data
,Open,High,Low,Close,Adj Close,Volume,Singapore
2015-10-01,2795.399902,3104.719971,2765.439941,2998.350098,2998.350098,0.0,
2015-11-01,2976.719971,3043.850098,2843.949951,2855.939941,2855.939941,0.0,
2015-12-01,2862.790039,2911.439941,2793.389893,2882.72998,2882.72998,0.0,
2016-01-01,2889.22998,2890.209961,2529.01001,2629.110107,2629.110107,0.0,
2016-02-01,2637.050049,2684.790039,2528.439941,2666.51001,2666.51001,0.0,
2016-03-01,2666.709961,2906.800049,2654.97998,2840.899902,2840.899902,0.0,
2016-04-01,2820.659912,2964.100098,2783.419922,2838.52002,2838.52002,158708700.0,
2016-05-01,2842.860107,2848.899902,2713.469971,2791.060059,2791.060059,0.0,
2016-06-01,2787.98999,2881.919922,2703.47998,2840.929932,2840.929932,0.0,
2016-07-01,2848.449951,2958.899902,2830.0,2868.689941,2868.689941,0.0,
2016-08-01,2875.590088,2898.27002,2810.8798829999996,2820.590088,2820.590088,0.0,
2016-09-01,2821.929932,2911.840088,2791.3798829999996,2869.469971,2869.469971,0.0,
2016-10-01,2879.850098,2901.72998,2783.330078,2813.8701170000004,2813.8701170000004,0.0,
2016-11-01,2814.080078,2915.419922,2760.969971,2905.169922,2905.169922,0.0,
2016-12-01,2913.649902,2980.77002,2857.909912,2880.76001,2880.76001,0.0,
2017-01-01,2887.0,3065.1298829999996,2869.659912,3046.800049,3046.800049,0.0,
2017-02-01,3045.939941,3138.969971,3030.649902,3096.610107,3096.610107,4018227800.0,
2017-03-01,3106.300049,3188.02002,3104.330078,3175.110107,3175.110107,5462555700.0,
2017-04-01,3180.27002,3189.810059,3113.899902,3175.439941,3175.439941,4292226700.0,
2017-05-01,3183.429932,3275.389893,3183.409912,3210.820068,3210.820068,5080433500.0,
2017-06-01,3214.1201170000004,3270.919922,3196.48999,3226.47998,3226.47998,4414015100.0,
2017-07-01,3228.909912,3354.709961,3196.139893,3329.52002,3329.52002,5085548600.0,
2017-08-01,3321.5,3349.090088,3244.22998,3277.26001,3277.26001,4856835500.0,
2017-09-01,3274.389893,3275.139893,3193.409912,3219.909912,3219.909912,3840282400.0,
2017-10-01,3233.949951,3392.149902,3230.810059,3374.080078,3374.080078,4261116400.0,
2017-11-01,3377.1899409999996,3449.320068,3341.300049,3433.540039,3433.540039,4789747800.0,
2017-12-01,3441.850098,3469.360107,3370.219971,3402.919922,3402.919922,3386126700.0,
2018-01-01,3406.4799799999996,3611.6899409999996,3403.8701170000004,3533.98999,3533.98999,4727173600.0,
2018-02-01,3536.929932,3574.5900880000004,3340.550049,3517.9399409999996,3517.9399409999996,6143735500.0,
2018-03-01,3493.4399409999996,3555.9799799999996,3382.780029,3427.969971,3427.969971,4963081900.0,
2018-04-01,3439.040039,3628.429932,3338.959961,3613.929932,3613.929932,4599803900.0,
2018-05-01,3624.1999509999996,3641.649902,3428.179932,3428.179932,3428.179932,5918362800.0,
2018-06-01,3423.5,3492.3400880000004,3237.77002,3268.699951,3268.699951,5500961400.0,
2018-07-01,3277.429932,3341.419922,3176.26001,3319.850098,3319.850098,5029346600.0,
2018-08-01,3331.050049,3347.97998,3187.830078,3213.47998,3213.47998,5005791600.0,
2018-09-01,3209.969971,3265.01001,3102.72998,3257.050049,3257.050049,4158150600.0,
2018-10-01,3262.429932,3272.8798829999996,2955.679932,3018.800049,3018.800049,5516696000.0,
2018-11-01,3045.679932,3132.419922,3007.310059,3117.610107,3117.610107,4457632700.0,
2018-12-01,3154.219971,3192.8798829999996,3000.449951,3068.76001,3068.76001,3627597800.0,
2019-01-01,3072.98999,3250.27002,2993.419922,3190.169922,3190.169922,4467841200.0,
2019-02-01,3194.219971,3286.080078,3174.0,3212.689941,3212.689941,3786000800.0,
2019-03-01,3210.840088,3251.719971,3156.790039,3212.8798829999996,3212.8798829999996,4128594600.0,
2019-04-01,3229.110107,3415.179932,3227.6201170000004,3400.1999509999996,3400.1999509999996,4447727600.0,
2019-05-01,3389.5200200000004,3397.179932,3110.51001,3117.76001,3117.76001,4319537800.0,
2019-06-01,3111.51001,3336.080078,3104.030029,3321.610107,3321.610107,4160448600.0,
2019-07-01,3339.580078,3386.649902,3299.889893,3300.75,3300.75,4489792100.0,
2019-08-01,3282.790039,3311.26001,3040.159912,3106.52002,3106.52002,5146051500.0,
2019-09-01,3092.25,3216.8701170000004,3074.040039,3119.98999,3119.98999,4116898900.0,
2019-10-01,3130.110107,3235.23999,3068.830078,3229.8798829999996,3229.8798829999996,4402690200.0,
2019-11-01,3227.600098,3285.719971,3182.050049,3193.919922,3193.919922,7055882400.0,
2019-12-01,3198.27002,3239.23999,3144.070068,3222.830078,3222.830078,4536740600.0,
2020-01-01,3230.47998,3283.889893,3144.100098,3153.72998,3153.72998,4951167700.0,
2020-02-01,3131.02002,3233.860107,3008.459961,3011.080078,3011.080078,5320489700.0,
2020-02-21,,,,,,,24.0
2020-02-25,,,,,,,
2020-02-28,,,,,,,22.0
2020-03-01,2988.350098,3047.790039,2208.419922,2481.22998,2481.22998,7767702900.0,
2020-03-02,,,,,,,
2020-03-03,,,,,,,
2020-03-06,,,,,,,23.0
2020-03-10,,,,,,,
2020-03-13,,,,,,,21.0
2020-03-17,,,,,,,
2020-03-20,,,,,,,24.0
2020-03-23,,,,,,,
2020-03-24,,,,,,,
2020-03-27,,,,,,,27.0
2020-03-30,,,,,,,
2020-03-31,,,,,,,
2020-04-01,2468.169922,2671.580078,2380.840088,2624.22998,2624.22998,7238328000.0,
2020-04-03,,,,,,,37.0
2020-04-06,,,,,,,
2020-04-07,,,,,,,
2020-04-10,,,,,,,73.0
2020-04-13,,,,,,,
2020-04-14,,,,,,,
2020-04-17,,,,,,,85.0
2020-04-20,,,,,,,
2020-04-21,,,,,,,
2020-04-24,,,,,,,90.0
2020-04-27,,,,,,,
2020-04-28,,,,,,,
2020-05-01,2555.669922,2611.73999,2489.939941,2510.75,2510.75,7367276100.0,90.0
2020-05-05,,,,,,,
2020-05-15,,,,,,,
2020-05-21,,,,,,,
2020-05-22,,,,,,,92.0
2020-05-25,,,,,,,
2020-05-26,,,,,,,
2020-05-30,,,,,,,
2020-06-01,2519.419922,2839.389893,2516.459961,2589.909912,2589.909912,8396435700.0,
2020-06-05,,,,,,,89.0
2020-06-08,,,,,,,
2020-06-15,,,,,,,
2020-06-16,,,,,,,
2020-06-19,,,,,,,92.0
2020-06-22,,,,,,,
2020-06-25,,,,,,,
2020-07-01,2604.080078,2707.669922,2511.02002,2529.820068,2529.820068,4876221500.0,
2020-07-03,,,,,,,
2020-07-06,,,,,,,
2020-07-07,,,,,,,90.0
2020-07-12,,,,,,,
2020-07-14,,,,,,,
2020-07-20,,,,,,,92.0
2020-07-26,,,,,,,
2020-07-27,,,,,,,
2020-07-31,,,,,,,
2020-08-01,2522.530029,2602.330078,2478.389893,2532.51001,2532.51001,6347053700.0,
2020-08-03,,,,,,,88.0
2020-08-07,,,,,,,
2020-08-10,,,,,,,
2020-08-12,,,,,,,
2020-08-14,,,,,,,90.0
2020-08-17,,,,,,,
2020-08-25,,,,,,,
2020-08-28,,,,,,,90.0
2020-08-31,,,,,,,
2020-09-01,2521.810059,2546.8701170000004,2476.820068,2490.090088,2490.090088,2000718800.0,
2020-09-11,2481.080078,2492.419922,2476.820068,2490.090088,2490.090088,0.0,
解决方案
- Use
pandas.read_csv, withparse_datesandindex_colset to the unnamed date column at index 0. .dropnawithhow='all', which will drop any row that's entirelyNaN. The index isn't considered, which is why the date column is set as the index.- The dates don't technically have to be parsed to a datetime, but this is financial data so it should be in a correct datetime format for timeseries analysis, and because it will plot correctly. The date column has to be the index to easily
.dropnain this manner.
df = pd.read_csv('testing.csv', parse_dates=[0], index_col=0)
# drop na
df = df.dropna(how='all')
# save file
df.to_csv('test_updated.csv', index=True)
# display(df)
Open High Low Close Adj Close Volume Singapore
2015-10-01 2795.39990 3104.71997 2765.43994 2998.35010 2998.35010 0.00000e+00 NaN
2015-11-01 2976.71997 3043.85010 2843.94995 2855.93994 2855.93994 0.00000e+00 NaN
2015-12-01 2862.79004 2911.43994 2793.38989 2882.72998 2882.72998 0.00000e+00 NaN
2016-01-01 2889.22998 2890.20996 2529.01001 2629.11011 2629.11011 0.00000e+00 NaN
2016-02-01 2637.05005 2684.79004 2528.43994 2666.51001 2666.51001 0.00000e+00 NaN
2016-03-01 2666.70996 2906.80005 2654.97998 2840.89990 2840.89990 0.00000e+00 NaN
2016-04-01 2820.65991 2964.10010 2783.41992 2838.52002 2838.52002 1.58709e+08 NaN
2016-05-01 2842.86011 2848.89990 2713.46997 2791.06006 2791.06006 0.00000e+00 NaN
2016-06-01 2787.98999 2881.91992 2703.47998 2840.92993 2840.92993 0.00000e+00 NaN
2016-07-01 2848.44995 2958.89990 2830.00000 2868.68994 2868.68994 0.00000e+00 NaN
2016-08-01 2875.59009 2898.27002 2810.87988 2820.59009 2820.59009 0.00000e+00 NaN
2016-09-01 2821.92993 2911.84009 2791.37988 2869.46997 2869.46997 0.00000e+00 NaN
2016-10-01 2879.85010 2901.72998 2783.33008 2813.87012 2813.87012 0.00000e+00 NaN
2016-11-01 2814.08008 2915.41992 2760.96997 2905.16992 2905.16992 0.00000e+00 NaN
2016-12-01 2913.64990 2980.77002 2857.90991 2880.76001 2880.76001 0.00000e+00 NaN
2017-01-01 2887.00000 3065.12988 2869.65991 3046.80005 3046.80005 0.00000e+00 NaN
2017-02-01 3045.93994 3138.96997 3030.64990 3096.61011 3096.61011 4.01823e+09 NaN
2017-03-01 3106.30005 3188.02002 3104.33008 3175.11011 3175.11011 5.46256e+09 NaN
2017-04-01 3180.27002 3189.81006 3113.89990 3175.43994 3175.43994 4.29223e+09 NaN
2017-05-01 3183.42993 3275.38989 3183.40991 3210.82007 3210.82007 5.08043e+09 NaN
2017-06-01 3214.12012 3270.91992 3196.48999 3226.47998 3226.47998 4.41402e+09 NaN
2017-07-01 3228.90991 3354.70996 3196.13989 3329.52002 3329.52002 5.08555e+09 NaN
2017-08-01 3321.50000 3349.09009 3244.22998 3277.26001 3277.26001 4.85684e+09 NaN
2017-09-01 3274.38989 3275.13989 3193.40991 3219.90991 3219.90991 3.84028e+09 NaN
2017-10-01 3233.94995 3392.14990 3230.81006 3374.08008 3374.08008 4.26112e+09 NaN
2017-11-01 3377.18994 3449.32007 3341.30005 3433.54004 3433.54004 4.78975e+09 NaN
2017-12-01 3441.85010 3469.36011 3370.21997 3402.91992 3402.91992 3.38613e+09 NaN
2018-01-01 3406.47998 3611.68994 3403.87012 3533.98999 3533.98999 4.72717e+09 NaN
2018-02-01 3536.92993 3574.59009 3340.55005 3517.93994 3517.93994 6.14374e+09 NaN
2018-03-01 3493.43994 3555.97998 3382.78003 3427.96997 3427.96997 4.96308e+09 NaN
2018-04-01 3439.04004 3628.42993 3338.95996 3613.92993 3613.92993 4.59980e+09 NaN
2018-05-01 3624.19995 3641.64990 3428.17993 3428.17993 3428.17993 5.91836e+09 NaN
2018-06-01 3423.50000 3492.34009 3237.77002 3268.69995 3268.69995 5.50096e+09 NaN
2018-07-01 3277.42993 3341.41992 3176.26001 3319.85010 3319.85010 5.02935e+09 NaN
2018-08-01 3331.05005 3347.97998 3187.83008 3213.47998 3213.47998 5.00579e+09 NaN
2018-09-01 3209.96997 3265.01001 3102.72998 3257.05005 3257.05005 4.15815e+09 NaN
2018-10-01 3262.42993 3272.87988 2955.67993 3018.80005 3018.80005 5.51670e+09 NaN
2018-11-01 3045.67993 3132.41992 3007.31006 3117.61011 3117.61011 4.45763e+09 NaN
2018-12-01 3154.21997 3192.87988 3000.44995 3068.76001 3068.76001 3.62760e+09 NaN
2019-01-01 3072.98999 3250.27002 2993.41992 3190.16992 3190.16992 4.46784e+09 NaN
2019-02-01 3194.21997 3286.08008 3174.00000 3212.68994 3212.68994 3.78600e+09 NaN
2019-03-01 3210.84009 3251.71997 3156.79004 3212.87988 3212.87988 4.12859e+09 NaN
2019-04-01 3229.11011 3415.17993 3227.62012 3400.19995 3400.19995 4.44773e+09 NaN
2019-05-01 3389.52002 3397.17993 3110.51001 3117.76001 3117.76001 4.31954e+09 NaN
2019-06-01 3111.51001 3336.08008 3104.03003 3321.61011 3321.61011 4.16045e+09 NaN
2019-07-01 3339.58008 3386.64990 3299.88989 3300.75000 3300.75000 4.48979e+09 NaN
2019-08-01 3282.79004 3311.26001 3040.15991 3106.52002 3106.52002 5.14605e+09 NaN
2019-09-01 3092.25000 3216.87012 3074.04004 3119.98999 3119.98999 4.11690e+09 NaN
2019-10-01 3130.11011 3235.23999 3068.83008 3229.87988 3229.87988 4.40269e+09 NaN
2019-11-01 3227.60010 3285.71997 3182.05005 3193.91992 3193.91992 7.05588e+09 NaN
2019-12-01 3198.27002 3239.23999 3144.07007 3222.83008 3222.83008 4.53674e+09 NaN
2020-01-01 3230.47998 3283.88989 3144.10010 3153.72998 3153.72998 4.95117e+09 NaN
2020-02-01 3131.02002 3233.86011 3008.45996 3011.08008 3011.08008 5.32049e+09 NaN
2020-02-21 NaN NaN NaN NaN NaN NaN 24.0
2020-02-28 NaN NaN NaN NaN NaN NaN 22.0
2020-03-01 2988.35010 3047.79004 2208.41992 2481.22998 2481.22998 7.76770e+09 NaN
2020-03-06 NaN NaN NaN NaN NaN NaN 23.0
2020-03-13 NaN NaN NaN NaN NaN NaN 21.0
2020-03-20 NaN NaN NaN NaN NaN NaN 24.0
2020-03-27 NaN NaN NaN NaN NaN NaN 27.0
2020-04-01 2468.16992 2671.58008 2380.84009 2624.22998 2624.22998 7.23833e+09 NaN
2020-04-03 NaN NaN NaN NaN NaN NaN 37.0
2020-04-10 NaN NaN NaN NaN NaN NaN 73.0
2020-04-17 NaN NaN NaN NaN NaN NaN 85.0
2020-04-24 NaN NaN NaN NaN NaN NaN 90.0
2020-05-01 2555.66992 2611.73999 2489.93994 2510.75000 2510.75000 7.36728e+09 90.0
2020-05-22 NaN NaN NaN NaN NaN NaN 92.0
2020-06-01 2519.41992 2839.38989 2516.45996 2589.90991 2589.90991 8.39644e+09 NaN
2020-06-05 NaN NaN NaN NaN NaN NaN 89.0
2020-06-19 NaN NaN NaN NaN NaN NaN 92.0
2020-07-01 2604.08008 2707.66992 2511.02002 2529.82007 2529.82007 4.87622e+09 NaN
2020-07-07 NaN NaN NaN NaN NaN NaN 90.0
2020-07-20 NaN NaN NaN NaN NaN NaN 92.0
2020-08-01 2522.53003 2602.33008 2478.38989 2532.51001 2532.51001 6.34705e+09 NaN
2020-08-03 NaN NaN NaN NaN NaN NaN 88.0
2020-08-14 NaN NaN NaN NaN NaN NaN 90.0
2020-08-28 NaN NaN NaN NaN NaN NaN 90.0
2020-09-01 2521.81006 2546.87012 2476.82007 2490.09009 2490.09009 2.00072e+09 NaN
2020-09-11 2481.08008 2492.41992 2476.82007 2490.09009 2490.09009 0.00000e+00 NaN
Plotting
- This plot uses
pandas.DataFrame.plot, which usesmatplotlibas the default plot engine- Note that this wasn't drawing lines between the NaN values, so
dropnawas added for doing the plot.
- Note that this wasn't drawing lines between the NaN values, so
- Don't plot
Volumewith the values, because the scale (y-value) is so much larger. 'Singapore'is plotted separately, because of it's lower value and few data points, it will look funny as a line plot.
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(9, 10))
df[['Open', 'High', 'Low', 'Close', 'Adj Close']].dropna().plot(ax=ax1)
ax2.scatter(df.index, 'Singapore', data=df, label='Singapore')
ax2.legend()
plt.show()
这篇关于如何在除了日期列的所有行都为NaN的地方dropna?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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