convert a data frame column values to list
import pandas as pd
data_dict = {'one': pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two': pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(data_dict)
print(f"DataFrame:n{df}n")
print(f"column types:n{df.dtypes}")
col_one_list = df['one'].tolist()
col_one_arr = df['one'].to_numpy()
print(f"ncol_one_list:n{col_one_list}ntype:{type(col_one_list)}")
print(f"ncol_one_arr:n{col_one_arr}ntype:{type(col_one_arr)}")convert all columns to float pandas
You have four main options for converting types in pandas:
to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)
astype() - convert (almost) any type to (almost) any other type (even if it's not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).
infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.
convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas' object to indicate a missing value).
Read on for more detailed explanations and usage of each of these methods.Copyright © 2021 Codeinu
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