Question

我有以下格式的csv文件：

@Override
public void onDataChange(DataSnapshot dataSnapshot) {
    fName = dataSnapshot.child("fName").getValue(String.class);
    role = dataSnapshot.child("role").getValue(String.class);

    welcomeText.setText("Welcome " + fName + "! You are logged-in as " + role);
}

我想将其读取到熊猫DF中，然后将其融合为以下格式：

| a  | b  | 2018 | 2018 | 2019 | 2019 |
|    |    | jan  | feb  | jan  | feb  |
---------------------------------------
| a1 | b1 | 0    | 1    | 2    | 3    |
| a1 | b2 | 4    | 5    | 6    | 7    |
| a2 | b1 | 8    | 9    | 10   | 11   |
| a2 | b2 | 12   | 13   | 14   | 15   |

如何实现？

Answer 1

在使用普通数据框的情况下，这应该可以工作：

import pandas as pd


df = pd.DataFrame({
    'a': ['a1', 'a1', 'a2', 'a2',],
    'b': ['b1', 'b2', 'b2', 'b2',],
    '2018 jan': [0, 4, 8, 12],
    '2018 feb': [1, 5, 9, 13],
    '2019 jan': [2, 6, 10, 14],
    '2019 feb': [3, 7, 11, 15],    
})

df = df.melt(id_vars=['a', 'b'], var_name='date', value_name='value')
df['date'] = df['date'].str.split(' ')
df['year'] = df['date'].str[0]
df['month'] = df['date'].str[1]
df.drop(columns='date', inplace=True)

输出：

    a   b  value  year month
0   a1  b1      0  2018   jan
1   a1  b2      4  2018   jan
2   a2  b2      8  2018   jan
3   a2  b2     12  2018   jan
4   a1  b1      1  2018   feb
5   a1  b2      5  2018   feb
6   a2  b2      9  2018   feb
7   a2  b2     13  2018   feb
8   a1  b1      2  2019   jan
9   a1  b2      6  2019   jan
10  a2  b2     10  2019   jan
11  a2  b2     14  2019   jan
12  a1  b1      3  2019   feb
13  a1  b2      7  2019   feb
14  a2  b2     11  2019   feb
15  a2  b2     15  2019   feb

如果注释中提到的列中有多个索引，则可以将其转换为普通数据框：

df = pd.read_csv('file.csv', header=[0,1])
df.columns = [' '.join(col).strip() for col in df.columns.values]
df.rename(columns={'a Unnamed: 0_level_1': 'a', 'b Unnamed: 1_level_1': 'b'}, inplace=True)

Answer 2

@KOB我的回答通常可以适合任何具有2行标题的csv文件，其中部分列仅在第一行，部分在第一行和第二行。根据您的问题，此代码将按要求正确放置所有标头。读取csv和创建的MulitIndex数据框时：

df_multiidx = pd.read_csv('two_levels_header_file.csv', header=[0,1])
id_vars = [idv for idv in df_multiidx.columns if 'Unnamed' in idv[1]]
value_vars = [valv for valv in df_multiidx.columns if 'Unnamed' not in valv[1]]
df_multiidx= df_multiidx.melt(id_vars=id_vars, value_vars=value_vars,var_name=['year','month'])
df_multiidx.rename(columns={col_ren:col_ren[0] for col_ren in id_vars})

输出：

    a   b   year    month   value
0   a1  b1  2018    jan 0
1   a1  b2  2018    jan 4
2   a2  b1  2018    jan 8
3   a2  b2  2018    jan 12
4   a1  b1  2018    feb 1
5   a1  b2  2018    feb 5
6   a2  b1  2018    feb 9
7   a2  b2  2018    feb 13
8   a1  b1  2019    jan 2
9   a1  b2  2019    jan 6
10  a2  b1  2019    jan 10
11  a2  b2  2019    jan 14
12  a1  b1  2019    feb 3
13  a1  b2  2019    feb 7
14  a2  b1  2019    feb 11
15  a2  b2  2019    feb 15

熊猫在MultiIndex列上融化

2 个答案: