pandas.DataFrame.melt# DataFrame.melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)[source]# Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured va
pandas.get_dummies# pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)[source]# Convert categorical variable into dummy/indicator variables. Each variable is converted in as many 0/1 variables as there are different values. Columns in the output are each named after a value; if the input is a DataFrame, the name of the or
pandas.DataFrame.fillna# DataFrame.fillna(value=None, *, method=None, axis=None, inplace=False, limit=None, downcast=_NoDefault.no_default)[source]# Fill NA/NaN values using the specified method. Parameters: valuescalar, dict, Series, or DataFrameValue to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く