Python基础篇—Pandas应用(二)

好的！接下来我们将利用骑行路线的数据集！我住在Montreal，加拿大东南部港市，我比较好奇这座城市的人们喜欢乘坐公共车辆，还是喜欢骑车？骑车的话 ，是喜欢在周末，还是工作日呢？

加载数据

首先，我们需要载入数据。

%matplotlib inline

import pandas as pd
import matplotlib.pyplot as plt

pd.set_option("display.mpl_style", "default") # Make the graphs a bit prettier
plt.rcParams["figure.figsize"] = (15, 5)
plt.rcParams["font.family"] = "sans-serif"

# This is necessary to show lots of columns in pandas 0.12. 
# Not necessary in pandas 0.13.
pd.set_option("display.width", 5000) 
pd.set_option("display.max_columns", 60)

bikes = pd.read_csv("../data/bikes.csv", sep=";", encoding="latin1", parse_dates=["Date"], dayfirst=True, index_col="Date")

我们来看一下数据：

bikes[:5]

输出：

数据给出的是该地区平均每天选择7种骑行路线的人数统计。
接下来，我们将主要考虑Berri这条路线。Berri是Montreal的一条想当不错的骑行街道。现在，我经常选择这条路线去图书馆，但是之前我在Old Montreal住的时候，也经过这条街道去上班。

添加新的一列到数据框

我们将建立一个数据框，该数据框里只包含Berri路线。

berri_bikes = bikes[["Berri 1"]].copy()
berri_bikes[:5]

输出：

接下来，我们需要为其增加一个“weekday”列。首先， 我们可以从索引来获得。索引就是位于数据框左侧“Date”下面的一列，它包含了一年中所有的日期。可以通过下面的命令查看：

berri_bikes.index

输出：

<class "pandas.tseries.index.DatetimeIndex">
[2012-01-01, ..., 2012-11-05]
Length: 310, Freq: None, Timezone: None

你可以看到，这里一共记录了一年中的310天的记录。Pandas拥有一系列的时间序列函数，所以，如果我们想要每一行记录数据的日期，我们可以这样做：

berri_bikes.index.day

输出：

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
       21, 22, 23, 24, 25, 26, 27, 28, 29,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
       26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,
       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,  1,  2,  3,  4,  5,
        6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
       23, 24, 25, 26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
       26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,
       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,
        2,  3,  4,  5], dtype=int32)

事实上，我们想得到的是工作日，即周几：

berri_bikes.index.weekday

输出：

array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,
       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,
       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,
       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,
       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,
       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,
       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,
       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,
       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,
       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,
       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,
       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,
       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,
       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype=int32)

这样就得到了weekday，其中，“0”代表周一。既然知道了如何获取weekday，我们就可以将其添加到数据框中，想这样：

berri_bikes.loc[:,"weekday"] = berri_bikes.index.weekday
berri_bikes[:5]

通过weekday将骑自行车的人加到一起

这是非常简单的！
数据框中有一个“.groupby()”的方法，如果你熟悉SQL的话，它就如同SQL中的groupby。这里不再详细地介绍这个方法——如果你想了解更多关于这个方法的知识，这个网页是个不错的选择！
“berri_bikes.groupby(‘weekday’).aggregate(sum)”的意思就是：按weekday将所有的行分组，然后再将相同的weekday的人数加起来：

weekday_counts = berri_bikes.groupby("weekday").aggregate(sum)
weekday_counts

输出：

用0到6表示星期不方便记忆，所以可以这样：

weekday_counts.index = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
weekday_counts

输出：

可视化:

weekday_counts.plot(kind="bar")

输出：