Question

我找到了parse html与pandas的绝佳方式。我的数据是一种奇怪的格式（附在下面）。我想将这些数据拆分为2个单独的dataframes。

注意每个cell如何用,分隔...... 是否有任何真正有效的方法来拆分所有这些单元格并创建2个数据框架，一个用于标签，一个用于标签括号中的( value )

NumPy包含所有ufuncs，我可以在string dtypes上使用它们，因为它们可以np.array转换为DF.as_matrix() 1}？我试图避开for loops，我可以遍历所有索引并填充一个空数组，但这非常野蛮。

我正在使用Beaker Notebook btw，这真的很酷（强烈推荐）

#Set URL Destination
url = "http://www.reef.org/print/db/stats"

#Process raw table
DF_raw = pd.pandas.read_html(url)[0]

#Get start/end indices of table
start_label = "10 Most Frequent Species"; start_idx = (DF_raw.iloc[:,0] == start_label).argmax()
end_label = "Top 10 Sites for Species Richness"; end_idx = (DF_raw.iloc[:,0] == end_label).argmax()

#Process table
DF_freqSpecies = pd.DataFrame(
                              DF_raw.as_matrix()[(start_idx + 1):end_idx,:],
                              columns = DF_raw.iloc[0,:]
)
DF_freqSpecies

#Split these into 2 separate DataFrames

这是我天真的做法：

import re
DF_species = pd.DataFrame(np.zeros_like(DF_freqSpecies),columns=DF_freqSpecies.columns)
DF_freq = pd.DataFrame(np.zeros_like(DF_freqSpecies).astype(str),columns=DF_freqSpecies.columns)

dims = DF_freqSpecies.shape
for i in range(dims[0]):
    for j in range(dims[1]):
        #Parse current dataframe
        species, freq = re.split("\s\(\d",DF_freqSpecies.iloc[i,j])
        freq = float(freq[:-1])
        #Populate split DataFrames
        DF_species.iloc[i,j] = species
        DF_freq.iloc[i,j] = freq

我想要这两个数据帧作为我的输出：

（1）物种; （2）频率

Answer 1

你可以这样做：

DF1：

In [182]: df1 = DF_freqSpecies.replace(r'\s*\(\d+\.*\d*\)', '', regex=True)

In [183]: df1.head()
Out[183]:
0 Tropical Western Atlantic California, Pacific Northwest and Alaska  \
0                  Bluehead                          Copper Rockfish
1                 Blue Tang                                  Lingcod
2      Stoplight Parrotfish                        Painted Greenling
3        Bicolor Damselfish                           Sunflower Star
4              French Grunt                          Plumose Anemone

0                      Hawaii Tropical Eastern Pacific  \
0               Saddle Wrasse           King Angelfish
1  Hawaiian Whitespotted Toby          Mexican Hogfish
2       Raccoon Butterflyfish               Barberfish
3            Manybar Goatfish            Flag Cabrilla
4                Moorish Idol   Panamic Sergeant Major

0              South Pacific Northeast US and Eastern Canada  \
0            Regal Angelfish                          Cunner
1  Bluestreak Cleaner Wrasse                 Winter Flounder
2           Manybar Goatfish                     Rock Gunnel
3             Brushtail Tang                         Pollock
4       Two-spined Angelfish                  Grubby Sculpin

0 South Atlantic States       Central Indo-Pacific
0         Slippery Dick               Moorish Idol
1       Belted Sandfish       Three-spot Dascyllus
2        Black Sea Bass  Bluestreak Cleaner Wrasse
3               Tomtate     Blacklip Butterflyfish
4                Cubbyu        Clark's Anemonefish

和DF2

In [193]: df2 = DF_freqSpecies.replace(r'.*\((\d+\.*\d*)\).*', r'\1', regex=True)

In [194]: df2.head()
Out[194]:
0 Tropical Western Atlantic California, Pacific Northwest and Alaska Hawaii  \
0                        85                                     54.6     92
1                      84.8                                     53.2   85.8
2                        81                                     50.8   85.7
3                      79.9                                     50.2   85.7
4                      74.8                                     49.7   82.9

0 Tropical Eastern Pacific South Pacific Northeast US and Eastern Canada  \
0                     85.7            79                            67.4
1                     82.5          77.3                            46.6
2                     75.2          73.9                            26.2
3                     68.9          73.3                            25.2
4                     67.9          72.8                            23.7

0 South Atlantic States Central Indo-Pacific
0                  79.7                 80.1
1                  78.5                 75.6
2                  78.5                 73.5
3                  72.7                 71.4
4                  65.7                 70.2

RegEx debugging and explanation:

我们基本上想删除所有内容，但括号中的数字除外：

(\d+\.*\d*) - group（1） - 这是我们的号码

\((\d+\.*\d*)\) - 括号中的数字

.*\((\d+\.*\d*)\).* - 整个事情 - 在'（'，'（'，我们的数字，'）'之前的任何事情，直到细胞结束的任何事情

它将被替换为组（1） - 我们的号码

Pandas DataFrame [cell =（label，value）]，分为2个独立的数据帧

1 个答案: