<p>考虑在csv中读取一个文本文件,在读取的文本文件中,每行的开始/结束引号都要去掉,这告诉解析器所有数据之间都是一个单数值。并使用内置的<code>StringIO</code>将文本字符串读入数据帧,而不是保存到磁盘以便导入。你知道吗</p>
<p>此外,跳过第一行重复的<em>总计</em>和<em>每场</em>甚至最后一行聚合,因为你可以用pandas来做。你知道吗</p>
<pre><code>from io import StringIO
import pandas as pd
with open('BasketballCSVQuotes.csv') as f:
csvdata = f.read().replace('"', '')
df = pd.read_csv(StringIO(csvdata), skiprows=1, skipfooter=1, engine='python')
print(df)
</code></pre>
<p><strong>输出</strong></p>
<pre class="lang-none prettyprint-override"><code> Rk Player Age G GS MP FG FGA 3P 3PA ... PTS FG% 3P% FT% MP.1 PTS.1 TRB.1 AST.1 STL.1 BLK.1
0 1.0 Kevin Durant\duranke01 29.0 5 5.0 182 54 107 9 28 ... 139 0.505 0.321 0.815 36.5 27.8 7.4 4.8 1.4 1.2
1 2.0 Klay Thompson\thompkl01 27.0 5 5.0 183 38 99 12 43 ... 99 0.384 0.279 1.000 36.7 19.8 6.4 1.8 0.2 0.4
2 3.0 Stephen Curry\curryst01 29.0 4 3.0 125 32 67 15 34 ... 98 0.478 0.441 1.000 31.2 24.5 5.3 3.5 2.0 0.5
3 4.0 Draymond Green\greendr01 27.0 5 5.0 186 27 55 8 20 ... 74 0.491 0.400 0.800 37.1 14.8 11.8 10.0 2.4 1.6
4 5.0 Andre Iguodala\iguodan01 34.0 5 4.0 140 14 29 4 12 ... 39 0.483 0.333 0.583 27.9 7.8 5.0 3.4 2.0 0.4
5 6.0 Quinn Cook\cookqu01 24.0 4 0.0 58 12 27 0 10 ... 30 0.444 0.000 0.750 14.4 7.5 2.3 1.0 0.3 0.0
6 7.0 Kevon Looney\looneke01 21.0 5 0.0 113 12 17 0 0 ... 28 0.706 NaN 0.500 22.6 5.6 5.8 1.0 0.8 0.2
7 8.0 Shaun Livingston\livinsh01 32.0 5 0.0 79 11 27 0 0 ... 26 0.407 NaN 1.000 15.9 5.2 1.2 2.4 0.0 0.2
8 9.0 David West\westda01 37.0 5 0.0 40 8 14 0 0 ... 16 0.571 NaN NaN 7.9 3.2 1.4 2.6 0.4 0.8
9 10.0 Nick Young\youngni01 32.0 4 2.0 41 3 11 3 10 ... 11 0.273 0.300 0.667 10.2 2.8 1.0 0.3 0.3 0.0
10 11.0 JaVale McGee\mcgeeja01 30.0 3 1.0 19 3 8 0 1 ... 6 0.375 0.000 NaN 6.2 2.0 2.0 0.0 0.0 0.3
11 12.0 Zaza Pachulia\pachuza01 33.0 2 0.0 8 1 2 0 0 ... 4 0.500 NaN 0.500 4.2 2.0 3.0 0.0 1.0 0.0
12 13.0 Jordan Belelljo01 23.0 4 0.0 23 1 4 0 0 ... 3 0.250 NaN 0.500 5.8 0.8 1.5 1.3 0.5 0.5
13 14.0 Damian Jones\jonesda03 22.0 1 0.0 3 0 1 0 0 ... 2 0.000 NaN 1.000 3.2 2.0 0.0 0.0 0.0 0.0
[14 rows x 30 columns]
</code></pre>