У меня есть список, содержащий кадры данных и строковые значения. Я хочу сбросить только строковые значения. Когда я поместил list.remove («Объем импорта (% изменения)»), я получил: Значение истинности для DataFrame неоднозначно. Используйте a.empty, a.bool (), a.item (), a.any () или a.all (). Я хочу код в форме l oop для многих случаев. Как это обобщить? Заранее спасибо
[ Country ISO3 Country Name Indicator Id \ 0 ABW Aruba 346 1 AFG Afghanistan 346 2 AGO Angola 346 3 ALB Albania 346 4 ARE United Arab Emirates 346 .. ... ... ... 167 VNM Vietnam 346 168 YEM Yemen, Rep. 346 169 ZAF South Africa 346 170 ZMB Zambia 346 171 ZWE Zimbabwe 346 Indicator Subindicator Type 1980 1981 \ 0 Gross national savings (% of GDP) % of GDP NaN NaN 1 Gross national savings (% of GDP) % of GDP NaN NaN 2 Gross national savings (% of GDP) % of GDP 17.704 15.105 3 Gross national savings (% of GDP) % of GDP 27.121 39.852 4 Gross national savings (% of GDP) % of GDP 45.757 43.159 .. ... ... ... ... 167 Gross national savings (% of GDP) % of GDP 12.270 8.971 168 Gross national savings (% of GDP) % of GDP NaN NaN 169 Gross national savings (% of GDP) % of GDP 35.318 28.097 170 Gross national savings (% of GDP) % of GDP 10.834 0.052 171 Gross national savings (% of GDP) % of GDP NaN NaN 1982 1983 1984 ... 2015 2016 2017 2018 2019 \ 0 NaN NaN NaN ... 15.466 17.221 17.044 18.203 19.872 1 NaN NaN NaN ... 21.342 25.728 22.462 21.856 17.526 2 12.419 11.463 14.437 ... 28.491 24.487 23.351 21.906 15.947 3 41.023 44.479 46.845 ... 15.761 16.966 17.430 17.906 17.880 4 37.663 36.592 40.707 ... 30.666 30.850 28.508 29.248 29.572 .. ... ... ... ... ... ... ... ... ... 167 10.955 11.829 12.266 ... 27.519 29.520 29.578 29.596 29.099 168 NaN NaN NaN ... -4.412 -1.366 1.956 5.084 6.783 169 22.165 26.711 23.121 ... 16.323 16.381 16.432 14.582 14.430 170 -0.655 6.240 7.016 ... 38.880 33.655 37.122 37.077 36.717 171 NaN NaN NaN ... 6.416 14.803 14.976 7.603 5.251 2020 2021 2022 2023 2024 0 19.228 18.487 17.654 16.826 15.808 1 17.440 17.754 20.424 20.528 20.543 2 18.147 18.359 19.004 19.650 20.219 3 18.103 17.731 17.247 16.727 16.257 4 28.179 27.200 26.531 26.046 25.829 .. ... ... ... ... ... 167 28.605 28.339 27.952 27.305 26.739 168 4.459 3.369 2.529 2.601 2.312 169 14.086 14.053 14.210 14.363 14.528 170 35.783 35.220 35.128 34.145 33.748 171 4.945 4.713 4.679 4.607 NaN [172 rows x 50 columns], Country ISO3 Country Name Indicator Id \ 0 ABW Aruba 347 1 AFG Afghanistan 347 2 AGO Angola 347 3 ALB Albania 347 4 ARE United Arab Emirates 347 .. ... ... ... 187 WSM Samoa 347 188 YEM Yemen, Rep. 347 189 ZAF South Africa 347 190 ZMB Zambia 347 191 ZWE Zimbabwe 347 Indicator Subindicator Type 1980 1981 \ 0 Inflation, average consumer prices Index NaN NaN 1 Inflation, average consumer prices Index NaN NaN 2 Inflation, average consumer prices Index NaN NaN 3 Inflation, average consumer prices Index NaN NaN 4 Inflation, average consumer prices Index 66.700 72.000 .. ... ... ... ... 187 Inflation, average consumer prices Index 13.930 16.787 188 Inflation, average consumer prices Index NaN NaN 189 Inflation, average consumer prices Index 4.308 4.967 190 Inflation, average consumer prices Index 0.005 0.006 191 Inflation, average consumer prices Index 160.147 169.046 1982 1983 1984 ... 2015 2016 2017 2018 \ 0 NaN NaN NaN ... 118.354 117.303 116.728 120.796 1 NaN NaN NaN ... 101.296 105.736 110.998 111.693 2 NaN NaN NaN ... 148.377 193.920 251.795 301.218 3 NaN NaN NaN ... 100.000 101.282 103.295 105.390 4 77.100 78.100 80.000 ... 265.654 269.951 275.261 283.739 .. ... ... ... ... ... ... ... ... 187 19.859 23.128 25.871 ... 109.117 109.259 110.677 114.741 188 NaN NaN NaN ... 498.322 435.717 543.184 770.088 189 5.683 6.392 7.117 ... 92.000 97.833 102.992 107.750 190 0.007 0.008 0.010 ... 155.818 183.660 195.740 209.525 191 170.043 155.671 152.780 ... 59.638 58.709 59.242 65.526 2019 2020 2021 2022 2023 2024 0 122.460 124.789 127.376 130.091 132.896 135.769 1 113.815 117.799 123.099 129.254 135.717 142.503 2 353.821 393.158 424.214 451.585 478.680 507.400 3 107.498 110.077 113.160 116.554 120.051 123.653 4 289.798 295.757 301.947 308.331 314.762 321.766 .. ... ... ... ... ... ... 187 120.639 125.766 130.168 133.813 137.560 141.411 188 924.105 993.413 1043.080 1095.240 1150.000 1207.500 189 113.090 119.198 125.754 132.670 139.967 147.666 190 232.049 259.895 286.534 315.188 346.707 381.377 191 113.638 124.281 128.880 132.746 136.728 140.830 ,'Volume of imports (% change)', 'Volume of exports (% change)', 'General government structural balance(% of GDP)', 'General government net debt(% of GDP)']
Я думаю, это то, что вы ищете. Фильтрует список для DataFrames.
input_list = [ pandas.DataFrame(), pandas.DataFrame(), "test_string", "another_string"] new_list = [list_entry for list_entry in input_list if type(list_entry) == pandas.DataFrame]