<p>我建议使用pandas,因为它可以帮助您更快地过滤和执行进一步的分析。在</p>
<pre><code># import pandas and datetime
import pandas as pd
import datetime
# read csv file
df = pd.read_csv("sample_data.csv")
# convert created_at from unix time to datetime
df['created_at'] = pd.to_datetime(df['created_at'], unit='s')
# contents of df at this point
# id created_at first_name last_name
# 0 1 2011-06-29 20:50:45 Cecelia Holt
# 1 2 2009-03-16 04:35:09 Emma Allison
# 2 3 2011-04-23 19:08:31 Desiree King
# 3 4 2009-01-05 17:15:16 Sam Davidson
# filtering example
df_filtered = df[(df['created_at'] <= datetime.date(2011,3,22))]
# output of df_filtered
# id created_at first_name last_name
# 1 2 2009-03-16 04:35:09 Emma Allison
# 3 4 2009-01-05 17:15:16 Sam Davidson
# filter based on dates mentioned in the question
df_filtered = df[(df['created_at'] >= datetime.date(2016,3,22)) & (df['created_at'] <= datetime.date(2016,4,15))]
# output of df_filtered would be empty at this point since the
# dates are out of this range
# sort
df_sorted = df_filtered.sort_values(['created_at'])
</code></pre>
<h3>熊猫过滤解释:</h3>
<p>首先需要知道的是,对dataframe使用比较运算符将返回一个带有布尔值的dataframe。在</p>
^{pr2}$
<p>会回来的</p>
<pre><code>False
False
True
True
</code></pre>
<p>现在,pandas支持逻辑索引。因此,如果将带有布尔值的数据帧传递给pandas,if将只返回与True相对应的值。在</p>
<pre><code>df[df['id'] > 2]
</code></pre>
<p>退货</p>
<pre><code>3 1303585711 Desiree King
4 1231175716 Sam Davidson
</code></pre>
<p>这就是你在熊猫身上很容易过滤的方法</p>