在 pandas 中创建包含列及其唯一值的数据框答案

【问题标题】：Create a dataframe with columns and their unique values in pandas在 pandas 中创建包含列及其唯一值的数据框
【发布时间】：2022-11-11 05:02:32
【问题描述】：

我试图寻找一种方法来创建列及其唯一值的数据框。我知道这用例较少，但将是初步了解唯一值的好方法。它看起来像这样......

State	County	City
Colorado	Denver	Denver
Colorado	El Paso	Colorado Springs
Colorado	Larimar	Fort Collins

变成了这个... |状态 |县 |城市 | | -------- | -------------- |------| |科罗拉多 |丹佛 |丹佛 | | |埃尔帕索 |科罗拉多斯普林斯 | | |拉里马尔 |柯林斯堡 | | | |拉夫兰 |

【问题讨论】：

标签： python pandas unique

【解决方案1】：

这是我想出的最好的解决方案，希望能帮助其他人寻找类似的东西！

def create_unique_df(df) -> pd.DataFrame:
    """ take a dataframe and creates a new one containing unique values for each column
    note, it only works for two columns or more

    :param df: dataframe you want see unique values for
    :param type: pandas.DataFrame
    return: dataframe of columns with unique values
    """
    # using list() allows us to combine lists down the line
    data_series = df.apply(lambda x: list( x.unique() ) )

    list_df = data_series.to_frame()

    # to create a df from lists they all neet to be the same leng. so we can append null 
    # values
    # to lists and make them the same length. First find differenc in length of longest list and
    # the rest
    list_df['needed_nulls'] = list_df[0].str.len().max() - list_df[0].str.len()

    # Second create a column of lists with one None value
    list_df['null_list_placeholder'] = [[None] for _ in range(list_df.shape[0])]

    # Third multiply the null list times the difference to get a list we can add to the list of
    # unique values making all the lists the same length. Example: [None] * 3  == [None, None, 
    # None]
    list_df['null_list_needed'] = list_df.null_list_placeholder * list_df.needed_nulls
    list_df['full_list'] = list_df[0] + list_df.null_list_needed

    unique_df = pd.DataFrame(
        list_df['full_list'].to_dict()
    )

    return unique_df

【讨论】：