作者：『博客园』山阴少年
www.cnblogs.com/jclian91/p/12305471.html
输入下面的命令查询pandas版本：
如果你还想知道pandas所依赖的模块的版本，你可以使用show_versions()函数:
你可以查看到Python，pandas, Numpy, matplotlib等的版本信息。
假设你需要创建一个示例DataFrame。有很多种实现的途径，我最喜欢的方式是传一个字典给DataFrame constructor，其中字典中的keys为列名，values为列的取值。
现在如果你需要创建一个更大的DataFrame，上述方法则需要太多的输入。在这种情况下，你可以使用Numpy的random.rand()函数，告诉它行数和列数，将它传递给DataFrame constructor：
这种方式很好，但如果你还想把列名变为非数值型的，你可以强制地将一串字符赋值给columns参数：
你可以想到，你传递的字符串的长度必须与列数相同。
让我们来看一下刚才我们创建的示例DataFrame:
我更喜欢在选取pandas列的时候使用点（.），但是这对那么列名中含有空格的列不会生效。让我们来修复这个问题。
更改列名最灵活的方式是使用rename()函数。你可以传递一个字典，其中keys为原列名，values为新列名，还可以指定axis:
df = df.rename({ col one : col_one , col two : col_two }, axis= columns )使用这个函数最好的方式是你需要更改任意数量的列名，不管是一列或者全部的列。
如果你需要一次性重新命令所有的列名，更简单的方式就是重写DataFrame的columns属性：
df.columns = [ col_one , col_two ]如果你需要做的仅仅是将空格换成下划线，那么更好的办法是使用str.replace()方法，这是因为你都不需要输入所有的列名：
df.columns = df.columns.str.replace( , _ )上述三个函数的结果都一样，可以更改列名使得列名中不含有空格：
最后，如果你需要在列名中添加前缀或者后缀，你可以使用add_prefix()函数：
或者使用add_suffix()函数：
让我们来看一下drinks这个DataFame:
| country | beer_servings | spirit_servings | wine_servings | total_litres_of_pure_alcohol | continent | |
|---|---|---|---|---|---|---|
| 0 | Afghanistan | 0 | 0 | 0 | 0.0 | Asia |
| 1 | Albania | 89 | 132 | 54 | 4.9 | Europe |
| 2 | Algeria | 25 | 0 | 14 | 0.7 | Africa |
| 3 | Andorra | 245 | 138 | 312 | 12.4 | Europe |
| 4 | Angola | 217 | 57 | 45 | 5.9 | Africa |
该数据集描述了每个国家的平均酒消费量。如果你想要将行序反转呢？
最直接的办法是使用loc函数并传递::-1，跟Python中列表反转时使用的切片符号一致：
| country | beer_servings | spirit_servings | wine_servings | total_litres_of_pure_alcohol | continent | |
|---|---|---|---|---|---|---|
| 192 | Zimbabwe | 64 | 18 | 4 | 4.7 | Africa |
| 191 | Zambia | 32 | 19 | 4 | 2.5 | Africa |
| 190 | Yemen | 6 | 0 | 0 | 0.1 | Asia |
| 189 | Vietnam | 111 | 2 | 1 | 2.0 | Asia |
| 188 | Venezuela | 333 | 100 | 3 | 7.7 | South America |
如果你还想重置索引使得它从0开始呢？
你可以使用reset_index()函数，告诉他去掉完全抛弃之前的索引：
| country | beer_servings | spirit_servings | wine_servings | total_litres_of_pure_alcohol | continent | |
|---|---|---|---|---|---|---|
| 0 | Zimbabwe | 64 | 18 | 4 | 4.7 | Africa |
| 1 | Zambia | 32 | 19 | 4 | 2.5 | Africa |
| 2 | Yemen | 6 | 0 | 0 | 0.1 | Asia |
| 3 | Vietnam | 111 | 2 | 1 | 2.0 | Asia |
| 4 | Venezuela | 333 | 100 | 3 | 7.7 | South America |
你可以看到，行序已经反转，索引也被重置为默认的整数序号。
跟之前的技巧一样，你也可以使用loc函数将列从左至右反转：
| continent | total_litres_of_pure_alcohol | wine_servings | spirit_servings | beer_servings | country | |
|---|---|---|---|---|---|---|
| 0 | Asia | 0.0 | 0 | 0 | 0 | Afghanistan |
| 1 | Europe | 4.9 | 54 | 132 | 89 | Albania |
| 2 | Africa | 0.7 | 14 | 0 | 25 | Algeria |
| 3 | Europe | 12.4 | 312 | 138 | 245 | Andorra |
| 4 | Africa | 5.9 | 45 | 57 | 217 | Angola |
逗号之前的冒号表示选择所有行，逗号之后的::-1表示反转所有的列，这就是为什么country这一列现在在最右边。
这里有drinks这个DataFrame的数据类型：
dtype: object假设你仅仅需要选取数值型的列，那么你可以使用select_dtypes()函数：
| beer_servings | spirit_servings | wine_servings | total_litres_of_pure_alcohol | |
|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0.0 |
| 1 | 89 | 132 | 54 | 4.9 |
| 2 | 25 | 0 | 14 | 0.7 |
| 3 | 245 | 138 | 312 | 12.4 |
| 4 | 217 | 57 | 45 | 5.9 |
这包含了int和float型的列。
你也可以使用这个函数来选取数据类型为object的列：
你还可以选取多种数据类型，只需要传递一个列表即可：
你还可以用来排除特定的数据类型：
让我们来创建另一个示例DataFrame:
这些数字实际上储存为字符型，导致其数据类型为object:
为了对这些列进行数学运算，我们需要将数据类型转换成数值型。你可以对前两列使用astype()函数：
但是，如果你对第三列也使用这个函数，将会引起错误，这是因为这一列包含了破折号（用来表示0）但是pandas并不知道如何处理它。
你可以对第三列使用to_numeric()函数，告诉其将任何无效数据转换为NaN:
如果你知道NaN值代表0，那么你可以fillna()函数将他们替换成0：
最后，你可以通过apply()函数一次性对整个DataFrame使用这个函数：
仅需一行代码就完成了我们的目标，因为现在所有的数据类型都转换成float:
pandas DataFrame被设计成可以适应内存，所以有些时候你可以减小DataFrame的空间大小，让它在你的系统上更好地运行起来。
这是drinks这个DataFrame所占用的空间大小：
可以看到它使用了30.4KB。
如果你对你的DataFrame有操作方面的问题，或者你不能将它读进内存，那么在读取文件的过程中有两个步骤可以使用来减小DataFrame的空间大小。
第一个步骤是只读取那些你实际上需要用到的列，可以调用usecols参数：
通过仅读取用到的两列，我们将DataFrame的空间大小缩小至13.6KB。
第二步是将所有实际上为类别变量的object列转换成类别变量，可以调用dtypes参数：
通过将continent列读取为category数据类型，我们进一步地把DataFrame的空间大小缩小至2.3KB。
值得注意的是，如果跟行数相比，category数据类型的列数相对较小，那么catefory数据类型可以减小内存占用。
假设你的数据集分化为多个文件，但是你需要将这些数据集读到一个DataFrame中。
举例来说，我有一些关于股票的小数聚集，每个数据集为单天的CSV文件。这是第一天的：
这是第二天的：
这是第三天的：
你可以将每个CSV文件读取成DataFrame，将它们结合起来，然后再删除原来的DataFrame，但是这样会多占用内存且需要许多代码
更好的方式为使用内置的glob模块。你可以给glob()函数传递某种模式，包括未知字符，这样它会返回符合该某事的文件列表。在这种方式下，glob会查找所有以stocks开头的CSV文件：
glob会返回任意排序的文件名，这就是我们为什么要用Python内置的sorted()函数来对列表进行排序。
我们以生成器表达式用read_csv()函数来读取每个文件，并将结果传递给concat()函数，这会将单个的DataFrame按行来组合：
不幸的是，索引值存在重复。为了避免这种情况，我们需要告诉concat()函数来忽略索引，使用默认的整数索引：
上一个技巧对于数据集中每个文件包含行记录很有用。但是如果数据集中的每个文件包含的列信息呢？
这里有一个例子，dinks数据集被划分成两个CSV文件，每个文件包含三列：
同上一个技巧一样，我们以使用glob()函数开始。这一次，我们需要告诉concat()函数按列来组合：
现在我们的DataFrame已经有六列了。
end
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data-darkmode-bgcolor="rgb(36, 36, 36)" data-darkmode-original-bgcolor="rgb(255, 255, 255)" data-darkmode-color="rgb(230, 230, 230)" data-darkmode-original-color="rgb(0, 0, 0)" data-darkmode-bgcolor-15923650965579="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15923650965579="rgb(255, 255, 255)" data-darkmode-color-15923650965579="rgb(230, 230, 230)" data-darkmode-original-color-15923650965579="rgb(0, 0, 0)" style="margin-right: 0em;margin-left: 0em;max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section style="margin-right: 0.5em;margin-left: 0.5em;max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-tool="mdnice编辑器" data-website="https://www.mdnice.com" style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><pre data-darkmode-bgcolor="rgb(36, 36, 36)" data-darkmode-original-bgcolor="rgb(255, 255, 255)" data-darkmode-color="rgb(168, 168, 168)" 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0.544px;box-sizing: border-box !important;overflow-wrap: break-word !important;"><pre style="max-width: 100%;letter-spacing: 0.544px;text-align: left;box-sizing: border-box !important;overflow-wrap: break-word !important;"><h1 style="max-width: 100%;white-space: pre-wrap;letter-spacing: 0.544px;font-family: 微软雅黑;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section mpa-from-tpl="t" style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><pre style="max-width: 100%;letter-spacing: 0.544px;line-height: inherit;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-mpa-template="t" mpa-from-tpl="t" style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-id="94086" data-color="#276ca3" data-tools="135编辑器" mpa-from-tpl="t" style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section mpa-from-tpl="t" style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><pre style="max-width: 100%;color: rgb(63, 63, 63);letter-spacing: 0.544px;line-height: inherit;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-mpa-template="t" mpa-from-tpl="t" style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-id="94086" data-color="#276ca3" data-tools="135编辑器" mpa-from-tpl="t" style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section mpa-from-tpl="t" style="max-width: 100%;display: inline-block;box-sizing: border-box !important;overflow-wrap: break-word !important;"><pre style="max-width: 100%;letter-spacing: 0.544px;line-height: inherit;box-sizing: border-box !important;overflow-wrap: break-word !important;"><pre data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-style="letter-spacing: 0.544px; background-color: rgb(255, 255, 255); text-align: center; color: rgba(230, 230, 230, 0.9); font-size: 16px; line-height: 25.6px; overflow-wrap: break-word !important;" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgba(230, 230, 230, 0.9)" data-darkmode-original-color-15882396318564="rgba(230, 230, 230, 0.9)" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgba(230, 230, 230, 0.9)" data-darkmode-original-color-15900529136199="rgba(230, 230, 230, 0.9)" style="max-width: 100%;letter-spacing: 0.544px;text-align: center;color: rgba(230, 230, 230, 0.9);line-height: 25.6px;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(230, 230, 230)" data-darkmode-original-color-15860613985508="rgb(0, 0, 0)" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(230, 230, 230)" data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" 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data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(230, 230, 230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" data-darkmode-bgimage-15900529136199="1" style="max-width: 100%;display: flex;justify-content: center;align-items: center;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(230, 230, 230)" data-darkmode-original-color-15860613985508="rgb(0, 0, 0)" data-darkmode-bgimage-15860613985508="1" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(230, 230, 230)" data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(230, 230, 230)" data-darkmode-original-color-15882384789136="rgb(0, 0, 0)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(230, 230, 230)" data-darkmode-original-color-15882396318564="rgb(0, 0, 0)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(230, 230, 230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" data-darkmode-bgimage-15900529136199="1" style="margin-left: 2px;max-width: 100%;width: 20px;box-sizing: border-box !important;overflow-wrap: break-word !important;"></section><section data-brushtype="text" data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(51, 51, 51)" data-darkmode-original-color-15860613985508="rgb(51, 51, 51)" data-darkmode-bgimage-15860613985508="1" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(51, 51, 51)" data-darkmode-original-color-15870356070738="rgb(51, 51, 51)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(51, 51, 51)" data-darkmode-original-color-15870356071023="rgb(51, 51, 51)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(51, 51, 51)" data-darkmode-original-color-15882384789136="rgb(51, 51, 51)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(51, 51, 51)" data-darkmode-original-color-15882396318564="rgb(51, 51, 51)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(51, 51, 51)" data-darkmode-original-color-15900529136199="rgb(51, 51, 51)" data-darkmode-bgimage-15900529136199="1" style="max-width: 100%;font-size: 14px;color: rgb(51, 51, 51);text-align: left;box-sizing: border-box !important;overflow-wrap: break-word !important;"><span style="max-width: 100%;font-family: 楷体, 楷体_GB2312, SimKai;white-space: pre-wrap;font-size: 12px;box-sizing: border-box !important;overflow-wrap: break-word !important;">点阅读原文,领优惠券</span><span style="letter-spacing: 0.544px;word-spacing: 1.6px;font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;font-size: 17px;text-align: justify;"></span></section></section></section></section></section></section></section>本篇文章来源于: 菜鸟学Python
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