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pd.read_excel(path, sheet_name='Sheet2', encoding='utf-16')
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df_excel = pd.ExcelFile(path)
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sheets = df_excel.sheet_names
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df_aba = df_excel.parse(<nome da aba>, skiprows=[1,2], header=None)
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pd.read_csv(path, sep=' ', header=None)
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my_dict = {'Computer':1500,'Monitor':300}
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df = pd.DataFrame(list(my_dict.items()),columns = ['Products','Prices'])
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For i, row in df.iterrows():
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print(row)
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row = row.replace({np.nan: None})
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pd[pd[2].isna()]
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dt_inicial = pd.Timestamp(2020, 1, 30)
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dt_final = pd.Timestamp(2020, 1, 31)
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df.loc[str(dt_inicial):str(dt_final)]
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OR
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df.loc['2002-1-1 01:00:00':'2002-1-1 04:00:00']
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diff = df1[~df1.astype(str).apply(tuple, 1).isin(df2.astype(str).apply(tuple, 1))]
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df.append(pd.Series(name='new row'))
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df['new column'] = np.nan
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df[2].map({'yes':1, 'no':0})
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pd.to_datetime(df[3], format="%Y%m%d%H")
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df.iloc[:,4].str.replace('.', '').str.replace(',', '.').astype(float, inplace=True)
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df[3].dt.date
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df.astype(int)
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f.drop([0, 1, 5], inplace=True)
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df.drop([2, 5'], axis=1)
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pd.Series(df[1].values, index=df[0]).to_dict()
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df.to_csv('file_out.csv', sep='\t', index=False, encoding='utf-8-sig')
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