te')); return $arr; } /* 遍历用户所有主题 * @param $uid 用户ID * @param int $page 页数 * @param int $pagesize 每页记录条数 * @param bool $desc 排序方式 TRUE降序 FALSE升序 * @param string $key 返回的数组用那一列的值作为 key * @param array $col 查询哪些列 */ function thread_tid_find_by_uid($uid, $page = 1, $pagesize = 1000, $desc = TRUE, $key = 'tid', $col = array()) { if (empty($uid)) return array(); $orderby = TRUE == $desc ? -1 : 1; $arr = thread_tid__find($cond = array('uid' => $uid), array('tid' => $orderby), $page, $pagesize, $key, $col); return $arr; } // 遍历栏目下tid 支持数组 $fid = array(1,2,3) function thread_tid_find_by_fid($fid, $page = 1, $pagesize = 1000, $desc = TRUE) { if (empty($fid)) return array(); $orderby = TRUE == $desc ? -1 : 1; $arr = thread_tid__find($cond = array('fid' => $fid), array('tid' => $orderby), $page, $pagesize, 'tid', array('tid', 'verify_date')); return $arr; } function thread_tid_delete($tid) { if (empty($tid)) return FALSE; $r = thread_tid__delete(array('tid' => $tid)); return $r; } function thread_tid_count() { $n = thread_tid__count(); return $n; } // 统计用户主题数 大数量下严谨使用非主键统计 function thread_uid_count($uid) { $n = thread_tid__count(array('uid' => $uid)); return $n; } // 统计栏目主题数 大数量下严谨使用非主键统计 function thread_fid_count($fid) { $n = thread_tid__count(array('fid' => $fid)); return $n; } ?>python - pandas remap new two columns based other column - Stack Overflow

科技改变生活-雨落星辰 - 所有的伟大,都源于一个勇敢的开始

    最新消息：雨落星辰是一个专注网站SEO优化、网站SEO诊断、搜索引擎研究、网络营销推广、网站策划运营及站长类的自媒体原创博客
    你的位置： 首页>programmer>python - pandas remap new two columns based other column - Stack Overflow

    python - pandas remap new two columns based other column - Stack Overflow

    programmeradmin3浏览0评论

    I have pandas table where I want to create new column and fill data based on another columns values. I also want to know, if new columns value is updated So I have dictionary like this:

    update_values = {"Groub_A": {"aadff2": "Mark", "aasd12": "Otto", "asdd2": "Jhon"},"Groub_B": {"aadfaa": "Josh", "aa1113": "Math", "967323sd": "Marek"}}

    And I want to my table look like this:

    Column_1 | Column_new_2 | Column_new_3
aadff2   | Mark         | Groub_A
aadff2   | Mark         | Groub_A
aasd12   | Otto         | Groub_A
asdd2    | Jhon         | Groub_A
967323sd | Marek        | Groub_B
967323sd | Marek        | Groub_B
aa1113   | Math         | Groub_B

    So far I have just copied Column_1 and use df.replace("Column_new_2":update_values["Groub_A"]) and same thing with groub_B, but then don't know how to make Column_new_3? There must be a easy solution, but I just can't figure it out.

    I have pandas table where I want to create new column and fill data based on another columns values. I also want to know, if new columns value is updated So I have dictionary like this:

    update_values = {"Groub_A": {"aadff2": "Mark", "aasd12": "Otto", "asdd2": "Jhon"},"Groub_B": {"aadfaa": "Josh", "aa1113": "Math", "967323sd": "Marek"}}

    And I want to my table look like this:

    Column_1 | Column_new_2 | Column_new_3
aadff2   | Mark         | Groub_A
aadff2   | Mark         | Groub_A
aasd12   | Otto         | Groub_A
asdd2    | Jhon         | Groub_A
967323sd | Marek        | Groub_B
967323sd | Marek        | Groub_B
aa1113   | Math         | Groub_B

    So far I have just copied Column_1 and use df.replace("Column_new_2":update_values["Groub_A"]) and same thing with groub_B, but then don't know how to make Column_new_3? There must be a easy solution, but I just can't figure it out.

    • python
    • pandas
    Share Improve this question edited yesterday President James K. Polk 42k28 gold badges109 silver badges145 bronze badges asked Feb 17 at 19:54 pinq-pinq- 3913 silver badges18 bronze badges
    Add a comment  | 

    3 Answers 3

    Reset to default 2

    Consider a nested list/dict comprehension to build a list of dictionaries to pass into pandas.DataFrame.from_records. Then, left merge against current data frame for New columns.

    new_data = [
    {"Column_1": k, "Column_new_2": v, "Column_new_3": gk}
    for gk, gv in update_values.items()
    for k, v in gv.items()
]

current_df = current_df.merge(
    pd.DataFrame.from_records(new_data), on = "Column_1", how = "left"
)
    

    import pandas as pd

# Original DataFrame
df = pd.DataFrame({
    'Column_1': ['aadff2', 'aadff2', 'aasd12', 'asdd2', '967323sd', '967323sd', 'aa1113']
})

# Dictionary with values to update
update_values = {"Groub_A": {"aadff2": "Mark", "aasd12": "Otto", "asdd2": "Jhon"},
                 "Groub_B": {"aadfaa": "Josh", "aa1113": "Math", "967323sd": "Marek"}}

# Create an empty list to store new rows
new_rows = []

# Iterate over the dictionary and create a new DataFrame
for group, values in update_values.items():
    for key, value in values.items():
        new_rows.append([key, value, group])

# Create a DataFrame from the new rows
df_update = pd.DataFrame(new_rows, columns=['Column_1', 'Column_new_2', 'Column_new_3'])

# Merge the original DataFrame with the new DataFrame
df = df.merge(df_update, on='Column_1', how='left')

print(df)

    Another possible solution, which uses map to create the two new columns:

    df.assign(
    **dict(zip(
        ['Column_2', 'Column_3'], 
        zip(*df['Column_1'].map(
            lambda x: [update_values["Groub_A"].get(x), 'Group_A'] 
            if x in update_values["Groub_A"] 
            else [update_values["Groub_B"].get(x), 'Group_B']
        ))
    ))
)

    Output:

       Column_1 Column_2 Column_3
0    aadff2     Mark  Group_A
1    aadff2     Mark  Group_A
2    aasd12     Otto  Group_A
3     asdd2     Jhon  Group_A
4  967323sd    Marek  Group_B
5  967323sd    Marek  Group_B
6    aa1113     Math  Group_B

    与本文相关的文章

    发布评论

    评论列表(0)

    1. 暂无评论