Question

我想要获得的是将多个单元格值划分为多行，然后只获取在fruit_weight列中具有较大数值的行。

我有以下格式：

fruit_type;fruit_color;fruit_weight
Apple|Banana;Red|Yellow;2|1
Orange;Orange;4
Pineapple|Grape|Watermelon;Brown|Purple|Green;12|1|15

所需的结果输出：

fruit_type;fruit_color;fruit_weight
Apple;Red;2
Orange;Orange;4
Watermelon;Green;15

我在想的是将细胞分成几行，然后解析这些值以获得正确的值，但我不知道如何开始。

一些帮助将不胜感激。

编辑1：

#!/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
fileData = pd.read_csv('articles.csv',delimiter=';')
fileData.replace('', np.nan, inplace=True)
fileData.dropna(subset=['fruit_type','fruit_color','fruit_weight'], inplace=True)
fileData = fileData.applymap(lambda x: x.split('|'))
idx = fileData.index.repeat(fileData.fruit_weight.str.len())
fileData =  fileData.apply(lambda x: pd.Series(np.concatenate(x.tolist())), 0)
print fileData
fileData.assign(idx=idx).groupby('idx', group_keys=False).apply(lambda x: x.sort_values('fruit_weight', ascending=False).head(1))

Answer 1

import pandas as pd
import numpy as np
import io

text = '''fruit_type;fruit_color;fruit_weight
Apple|Banana;Red|Yellow;2|1
Orange;Orange;4
Pineapple|Grape|Watermelon;Brown|Purple|Green;12|1|15'''

buf = io.StringIO(text)

df = pd.read_csv(buf, sep=';')   # replace "buf" with your CSV filename
df = df.applymap(lambda x: x.split('|'))

df

                       fruit_type             fruit_color fruit_weight
0                 [Apple, Banana]           [Red, Yellow]       [2, 1]
1                        [Orange]                [Orange]          [4]
2  [Pineapple, Grape, Watermelon]  [Brown, Purple, Green]  [12, 1, 15]

加载和设置后，使用apply + pd.Series + np.concatenate展平您的数据框。同时，创建一个索引，使下一步的分组变得容易。

idx = df.index.repeat(df.fruit_weight.str.len())

idx
Int64Index([0, 0, 1, 2, 2, 2], dtype='int64')

df =  df.apply(lambda x: pd.Series(np.concatenate(x.tolist())), 0)
df

   fruit_type fruit_color fruit_weight
0       Apple         Red            2
1      Banana      Yellow            1
2      Orange      Orange            4
3   Pineapple       Brown           12
4       Grape      Purple            1
5  Watermelon       Green           15

现在，请致电groupby + apply并从每组中提取一行，其中重量值最高。

df.assign(idx=idx).groupby('idx', group_keys=False)\
       .apply(lambda x: x.sort_values('fruit_weight', ascending=False).head(1))

   fruit_type fruit_color fruit_weight  idx
0       Apple         Red            2    0
2      Orange      Orange            4    1
5  Watermelon       Green           15    2

Answer 2

一种非常简单的方法就是这样：

#!/bin/env python

# hardcoding inputs for testing
inputs = [
    "Apple|Banana;Red|Yellow;2|1",
    "Orange;Orange;4",
    "Pineapple|Grape|Watermelon;Brown|Purple|Green;12|1|15"]

# iterate over the hardcoded inputs
for input in inputs:
    # split the input string into fruit properties
    [ fruit_types, fruit_colors, fruit_weights ] = input.split(";")

    # if there is more than one fruit, split the string into a list of fruit_types
    if "|" in fruit_types:
        # assuming that there is one color and one weight for each fruit type,
        # split the other fruit properties as well
        fruit_types = fruit_types.split("|")
        fruit_colors = fruit_colors.split("|")
        fruit_weights = fruit_weights.split("|")

        # get highest value
        max_weight = max(fruit_weights)
        # get index of highest values in fruit_weights list
        i = fruit_weights.index(max_weight)

        print("{};{};{}").format(fruit_types[i], fruit_colors[i], fruit_weights[i])

    # if there is no more than one fruit
    else:
        print("{};{};{}").format(fruit_types, fruit_colors, fruit_weights)

Answer 3

使用：

#create dataframe
df = pd.read_csv(filename, sep=';') 

#split all values
df = df.applymap(lambda x: x.split('|'))
print (df)
                       fruit_type             fruit_color fruit_weight
0                 [Apple, Banana]           [Red, Yellow]       [2, 1]
1                        [Orange]                [Orange]          [4]
2  [Pineapple, Grape, Watermelon]  [Brown, Purple, Green]  [12, 1, 15]

#get position of max weight
a = pd.DataFrame(df['fruit_weight'].values.tolist()).astype(float).idxmax(1).tolist()
print (a)
[0, 0, 2]

#convert df to dictionary
b = df.to_dict('list')
print (b)
{'fruit_weight': [['2', '1'], ['4'], ['12', '1', '15']], 
'fruit_color': [['Red', 'Yellow'], ['Orange'], ['Brown', 'Purple', 'Green']], 
 'fruit_type': [['Apple', 'Banana'], ['Orange'], ['Pineapple', 'Grape', 'Watermelon']]}

#extract values by position
a = {k: [k1[v1] for k1,v1 in zip(v, a)] for k, v in b.items()}
print (a)
{'fruit_weight': ['2', '4', '15'], 
'fruit_color': ['Red', 'Orange', 'Green'], 
'fruit_type': ['Apple', 'Orange', 'Watermelon']}

#create DataFrame
df = pd.DataFrame(a)
print (df)
  fruit_color  fruit_type fruit_weight
0         Red       Apple            2
1      Orange      Orange            4
2       Green  Watermelon           15

<强>计时：

df = pd.concat([df]*1000).reset_index(drop=True)


def col(df):
    df = df.applymap(lambda x: x.split('|'))
    idx = df.index.repeat(df.fruit_weight.str.len())
    df =  df.apply(lambda x: pd.Series(np.concatenate(x.tolist())), 0)
    return df.assign(idx=idx).groupby('idx', group_keys=False).apply(lambda x: x.sort_values('fruit_weight', ascending=False).head(1))

def jez(df):
    df = df.applymap(lambda x: x.split('|'))
    a = pd.DataFrame(df['fruit_weight'].values.tolist()).astype(float).idxmax(1).tolist()
    b = df.to_dict('list')
    a = {k: [k1[v1] for k1,v1 in zip(v, a)] for k, v in b.items()}
    return pd.DataFrame(a)

print (col(df))
print (jez(df))


In [229]: %timeit (col(df))
1 loop, best of 3: 1.58 s per loop

In [230]: %timeit (jez(df))
100 loops, best of 3: 19.3 ms per loop

Python将单元格中的多个值拆分为多行

3 个答案: