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krglkvrmn
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Mar 13, 2022
krglkvrmn
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krglkvrmn
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В целом норм. Из замечаний - не сделан файл со списом зависимостей и маловато работы с таблицами в EDA
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| # read dataframe | ||
| df = pd.read_csv('../data/train.csv') |
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Пути к файлам лучше всегда указывать через os.path.join
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| fig, ax = plt.subplots(2, 2, figsize=(12, 10)) | ||
| fig.suptitle('Distributions nucleotides over positions in reads', fontsize=18) | ||
| ax[0, 0].hist(df['A'].dropna(), bins=30) | ||
| ax[0, 1].hist(df['C'].dropna(), bins=30) | ||
| ax[1, 0].hist(df['T'].dropna(), bins=30) | ||
| ax[1, 1].hist(df['G'].dropna(), bins=30) | ||
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| ax[0, 0].set_title('A', fontsize=16) | ||
| ax[0, 1].set_title('C', fontsize=16) | ||
| ax[1, 0].set_title('T', fontsize=16) | ||
| ax[1, 1].set_title('G', fontsize=16) |
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Было бы удобно отобразить это на одном графике. Плохой вариант - наложенные гистограммы с прозрачностью. Более хороший - barplot с группировкой или stacked barplot
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| rrna_annotation_df = pd.read_csv(path_to_file, header=1, sep='\t') | ||
| rrna_annotation_df = pd.DataFrame(np.r_[np.array(rrna_annotation_df.columns)[np.newaxis, :], | ||
| rrna_annotation_df.values]) | ||
| rrna_annotation_df.columns = ['chromosome', 'source', 'type', 'start', 'end', | ||
| 'score', 'strand', 'phase', 'attributes'] | ||
| rrna_annotation_df[['start', 'end']] = rrna_annotation_df[['start', 'end']].astype(int) | ||
| rrna_annotation_df['score'] = rrna_annotation_df['score'].astype(float) |
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Я не совсем понял к чему весь этот огород с индексами. Чтобы убрать комменты?
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| rrna_annotation_df = pd.read_csv(path_to_file, header=1, sep='\t') | |
| rrna_annotation_df = pd.DataFrame(np.r_[np.array(rrna_annotation_df.columns)[np.newaxis, :], | |
| rrna_annotation_df.values]) | |
| rrna_annotation_df.columns = ['chromosome', 'source', 'type', 'start', 'end', | |
| 'score', 'strand', 'phase', 'attributes'] | |
| rrna_annotation_df[['start', 'end']] = rrna_annotation_df[['start', 'end']].astype(int) | |
| rrna_annotation_df['score'] = rrna_annotation_df['score'].astype(float) | |
| rrna_annotation_df = pd.read_table(path_to_file, header=None, names=['chromosome', 'source', 'type', 'start', 'end', 'score', 'strand', 'phase', 'attributes'], comment="#") | |
| rrna_annotation_df[['start', 'end']] = rrna_annotation_df[['start', 'end']].astype(int) | |
| rrna_annotation_df['score'] = rrna_annotation_df['score'].astype(float) |
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| rna_stats_df = rrna_annotation_df.groupby( | ||
| ['chromosome', 'rna_type'], as_index=False).size().reset_index(name='counts') |
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Мб дело в версии пандаса, но я проверить не мог, так как список зависимостей не указан)
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| is_inside = df_intersected[['start_x', 'end_x', 'start_y', 'end_y']].apply( | ||
| lambda x: x[0] > x[2] and x[1] < x[3], axis=1) |
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Можно было просто сделать сравнения между колонками, но так тоже можно
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