<h3>分别阅读标题</h3>
<p>使用<a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow noreferrer">^{<cd1>}</a>,您可以利用<code>nrows</code>、<code>skiprows</code>和<code>names</code>参数:</p>
<pre><code>from io import StringIO
x = """CustomerName,ClientID,EmailDate,EmailAddress
FNAME1,LNAME1,100,2019-01-13 00:00:00.000,FNAME1@HOTMAIL.COM
FNAME2,LNAME2,100,2019-01-13 00:00:00.000,FNAME2@GMAIL.COM
FNAME3,LNAME3,100,2019-01-13 00:00:00.000,FNAME3@AOL.COM
FNAME4,LNAME4,100,2019-01-13 00:00:00.000,FNAME40@GMAIL.COM
FNAME5,LNAME5,100,2019-01-13 00:00:00.000,FNAME5@AOL.COM"""
headers = pd.read_csv(StringIO(x), nrows=0).columns
headers = np.hstack((['FirstName', 'LastName'], headers[1:]))
df = pd.read_csv(StringIO(x), header=None, skiprows=[0], names=headers)
print(df)
# FirstName LastName ClientID EmailDate EmailAddress
# 0 FNAME1 LNAME1 100 2019-01-13 00:00:00.000 FNAME1@HOTMAIL.COM
# 1 FNAME2 LNAME2 100 2019-01-13 00:00:00.000 FNAME2@GMAIL.COM
# 2 FNAME3 LNAME3 100 2019-01-13 00:00:00.000 FNAME3@AOL.COM
# 3 FNAME4 LNAME4 100 2019-01-13 00:00:00.000 FNAME40@GMAIL.COM
# 4 FNAME5 LNAME5 100 2019-01-13 00:00:00.000 FNAME5@AOL.COM
</code></pre>