Pandas Read Dict. Column names are keys, values are dictionaries of index:data pairs >>> df.to_dict('dict') {'a': ['a', 'b', 'c', 'd']} >>> pd.dataframe.from_dict(data) col_1 col_2 0 3 a 1 2 b 2 1 c 3 0 d specify orient='index' to create the dataframe using dictionary keys as rows:
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Column names are keys, values are dictionaries of index:data pairs >>> df.to_dict('dict') {'a': Df ['b'] = df ['b'].apply (ast.literal_eval) print (df ['b'] [0] ['a']) # 3. Pd.dataframe.from_dict(dictionaryobject,orient='index') for your data you can convert it like below: A local file could be: A second option is to just use regex to parse the strings. Web df = pd.read_csv ('tmp.csv', dtype= {'b':object}) this will contain strings because pandas doesn't know what dictionaries are. ['a', 'b', 'c', 'd']} >>> pd.dataframe.from_dict(data) col_1 col_2 0 3 a 1 2 b 2 1 c 3 0 d specify orient='index' to create the dataframe using dictionary keys as rows: Web reading csv file as dictionary using pandas ask question asked 7 years, 7 months ago modified 7 years, 7 months ago viewed 22k times 4 i have the foll. If you want to pass in a path object, pandas accepts any os.pathlike. With this orient, keys are assumed to correspond to index values.
>>> df.type 0 3 1 4 2 5 3 6 4 3 5 4 6 5 7 6 8 3. >>> df.type 0 3 1 4 2 5 3 6 4 3 5 4 6 5 7 6 8 3. Examples by default the keys of the dict become the dataframe columns: If you want to pass in a path object, pandas accepts any os.pathlike. So you have to convert the strings first. A local file could be: Web convert the dataframe to a dictionary. Web df = pd.read_csv ('tmp.csv', dtype= {'b':object}) this will contain strings because pandas doesn't know what dictionaries are. This kind of data is best suited for pd.dataframe.from_dict. With this orient, keys are assumed to correspond to index values. Web reading csv file as dictionary using pandas ask question asked 7 years, 7 months ago modified 7 years, 7 months ago viewed 22k times 4 i have the foll.
