Python pandasによると、列は見つかりませんが、csvファイル内に存在します

• 問題は次のとおりです。
  • gene_mutation_df = df.groupby(['Hugo_Symbol']).apply(mutations_for_gene)
    • 'Hugo_Symbolはに使用されるためgroupby、列ではなくインデックスに含まれるようになりました
  • サンプルデータの場合、列のない空のデータフレームが作成されています。

    gene_mutation_df = df.groupby(['Hugo_Symbol']).apply(mutations_for_gene)
    print(gene_mutation_df)  # print the dataframe to see what it looks like
    print(gene_mutation_df.info())  # print the information for the dataframe
    gene_mutation_df.columns = gene_mutation_df.columns.str.strip()
    gene_mutation_df.set_index(['Hugo_Symbol', 'patient'], inplace=True)

# output

Empty DataFrame
Columns: [identifier, Hugo_Symbol, Tumor_Sample_Barcode, Variant_Classification, patient]
Index: []
Empty DataFrame
Columns: []
Index: []


<class 'pandas.core.frame.DataFrame'>
Index: 0 entries
Empty DataFrameNone

インデックスをリセットする

• インデックスをリセットするとHugo_Symbol、再び列が作成されます
• データフレームが空でない限り、KeyErrorを解決する必要があります。

    gene_mutation_df = gene_mutation_df.reset_index()  # try adding this line
    gene_mutation_df.set_index(['Hugo_Symbol', 'patient'], inplace=True)

追加メモ

• 数行のコードがあり、データフレームが空になる可能性があります
  • non_01_barcodes = df[~df['Tumor_Sample_Barcode'].str.contains(PRIMARY_TUMOR_PATIENT_ID_REGEX)]
  • shortened_patients = df['Tumor_Sample_Barcode'].str.extract(SHORTEN_PATIENT_REGEX, expand=False)
  • gene_mutation_df = df.groupby(['Hugo_Symbol']).apply(mutations_for_gene)

データフレームが空かどうかをテストします

• .emptyデータフレームが空かどうかを判断するために使用します

def prep_data(mutation_path):
    df = pd.read_csv(mutation_path, low_memory=True, dtype=str, header = 0)#Line 24 reads in a line memory csv file from the given path and parses it based on '\t' delimators, and casts the data to str
    
    df.columns = df.columns.str.strip()  # clean the column names here if there is leading or trailing whitespace.
    
    df = df[~df['Hugo_Symbol'].str.contains('Hugo_Symbol')] #analyzes the 'Hugo_Symbol' heading within the data and makes a new dataframe where any row that contains 'Hugo_Symbol' is dropped

    df['Hugo_Symbol'] = '\'' + df['Hugo_Symbol'].astype(str) # Appends ''\'' to all the data remaining in that column

    df['Tumor_Sample_Barcode'] = df['Tumor_Sample_Barcode'].str.strip() #strips away whitespace from the data within this heading
    non_silent = df.where(df['Variant_Classification'] != 'Silent') #creates a new dataframe where the data within the column 'Variant_Classification' is not equal to 'Silent'

    df = non_silent.dropna(subset=['Variant_Classification']) #Drops all the rows that are missing at least one element

    non_01_barcodes = df[~df['Tumor_Sample_Barcode'].str.contains(PRIMARY_TUMOR_PATIENT_ID_REGEX)]
    #TODO: Double check that the extra ['Tumor_Sample_Barcode'] serves no purpose
    df = df.drop(non_01_barcodes.index)
    print(df)
    shortened_patients = df['Tumor_Sample_Barcode'].str.extract(SHORTEN_PATIENT_REGEX, expand=False)
    df['identifier'] = shortened_patients

    gene_mutation_df = df.groupby(['Hugo_Symbol']).apply(mutations_for_gene)
    
    gene_mutation_df = gene_mutation_df.reset_index()  # reset the index here
    
    print(gene_mutation_df)
    
    if gene_mutation_df.empty:  # check if the dataframe is empty
        print('The dataframe is empty')
        
    else:
    
#         gene_mutation_df.set_index(['Hugo_Symbol', 'patient'], inplace=True)  # this is not needed, pivot won't work if you do this
#         gene_mutation_df = gene_mutation_df.reset_index()  # this is not needed, the dataframe was reset already
        gene_patient_mutations = gene_mutation_df.pivot(index='Hugo_Symbol', columns='patient', values='mutated')  # values needs to be a column in the dataframe

        return gene_patient_mutations.transpose().fillna(0)