Python数据分析之pandas常用命令整理！

￡神魔★判官ぃ 2023-02-13 08:17 7阅读 0赞

pandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现，它是使Python成为强大而高效的数据分析环境的重要因素之一。

**导入pandas库**：

import pandas as pd

**导入Series, DataFrame**：

from pandas import Series, DataFrame

**pandas常用命令**

**读取excel文件**：

df=pd.read_excel(fpath,sheet_name=name,usecols=[n,m,...])

![format_png][]

**创建数据表**：

![format_png 1][]

pd.DataFrame(dict, columns=dict.index, index=[dict.columnnum])

![format_png 2][]

**一、数据表信息查看**

**1.查看维度**：

df.shape

![format_png 3][]

**2.查看数据格式**

每一列数据的格式：

df.dtypes

![format_png 4][]

查看某一列数据的格式：

df['列名'].dtype

![format_png 5][]

**3.查看数据表基本信息**（列名称、数据格式、所占空间等）：

df.info()

![format_png 6][]

**4.判断数据是否是空值**：

判断整个数据表数据是否为空值：

df.isnull()

![format_png 7][]

判断某一列数据是否为空值：

df['列名'].isnull()

![format_png 8][]

**5.查看某一列的唯一值**：

df['列名'].unique()

![format_png 9][]

**6.查看数据表的值**：

df.values

![format_png 10][]

**7.查看数据表索引**：

df.index

![format_png 11][]

**8.查看列名称**：

df.columns

![format_png 12][]

**9.查看前n行数据**：

df.head(n)#默认前5行数据

![format_png 13][]

**10.查看后n行数据**：

df.tail(n)#默认后5行数据

![format_png 14][]

**二、数据清洗**

**1.用0填充NA**：

df.fillna(value=0)#生成副本，不影响原df,添加参数inplace=True修改原df

![format_png 15][]

**2.用列均值对列NA进行填充**：

df['列名'].fillna(df['列名'].mean())

![format_png 16][]

**3.删除含有缺失值的行**：

df.dropna()

![format_png 17][]

**4.更改某一列数据的数据格式**：

df['列名'].astype('int')

![format_png 18][]

**5.更改列名称**：

df.rename(columns={'原列名: '新列名'})

![format_png 19][]

**6.删除后出现的重复值**：

df['列名'].drop_duplicates()

![format_png 20][]

**7.删除先出现的重复值**：

df['列名'].drop_duplicates(keep='last')

![format_png 21][]

**8.数据替换**：

df['列名'].replace('原数据', '新数据')

![format_png 22][]

**三、数据处理**

数据读取

![format_png 23][]

**1.数据表合并**

1.1两表合并

合并列

df_inner=pd.merge(df1,df2,how='inner')#交集,类似sql语句inner join

![format_png 24][]

df_left=pd.merge(df1,df2,how='left')#类似sql语句left join

![format_png 25][]

df_right=pd.merge(df1,df2,how='right')#类似sql语句right join

![format_png 26][]

df_outer=pd.merge(df1,df2,how='outer')#并集

![format_png 27][]

合并行

pd.concat([df1,df2],axis=0,ignore_index=True)#ignore_index=True，重置索引，默认False

![format_png 28][]

1.2多表合并(合并行)

pd.concat([df1,df2,...],axis=0,ignore_index=True)

![format_png 29][]

**2.数据排序和排名**：

2.1数据排序

df.sort_values(by=['列名'])

![format_png 30][]

df.sort_values(by=['列1','列2'])

![format_png 31][]

2.2数据排名

df['列名'].rank()

**3.数据修改**

如果c\_adress列的值==‘山东汕头’，则显示为'广东'，否则显示为原数据

np.where(df1['c_adress']=='广东汕头','广东',df1['c_adress'])

**四、数据选取**

**1.选取单行：**df.loc\['行标签'\]、df.iloc\[n:\]

![format_png 32][]

**2.选取单列：**df\['列标签'\]、df.loc\[:,'列标签'\]df.iloc\[:,n\]

![format_png 33][]

**3.选取数据区域**

df.loc\[\['行标签1','行标签2',...\],\[列标签1','列标签2',...\]\]

df.iloc\[n:m,\[n,m,k,...\]\]

![format_png 34][]

**五、数据筛选**

**1.使用大于、小于、等于进行数据筛选**

**2.使用或(|)、与(&)、非(~)进行数据筛选**

**3.使用.isin进行数据筛选**

**4.使用query函数进行数据筛选**

**5.使用.str进行数据筛选**

![format_png 35][]

**六、分类汇总**

**1.统计每一列数据中非空的数量**

df.count()

![format_png 36][]

**2.按某字段对所有的列进行汇总计数**

df.groupby('字段').count()

![format_png 37][]

**3.按某字段对某列进行汇总计数**

df.groupby('字段')['列名'].count()

**4.按两个字段对某列进行汇总计数**

df.groupby(['字段1','字段2'])['列名'].count()

**5.按某字段进行分类计算某列的数量、合计和均值**

df.groupby('字段')['列名'].agg(len,np.sum,np.mean)

**七、统计分析**

创建数据表

![format_png 38][]

**1.数据抽样**

df.sample(n=None,frac=None,replace=False,weights=None,random_state=None, axis=None)[source]

1.1随机抽取n行

df.sample(n)

![format_png 39][]

1.2按比例抽取

df.sample(frac=n)

![format_png 40][]

1.3按抽样权重抽取

df.sample(n,weights=weights)

![format_png 41][]

1.4无放回抽样

df.sample(n,replace=False)

1.5有放回抽样

**2.计算最大值:max(axis=0/1)**

**3.计算最小值:min(axis=0/1)**

**4.计算均值:mean(axis=0/1)**

**5.计算标准差:std()**

**6.计算方差:var()**

**7.数据表描述性统计**

df.describe()

![format_png 42][]

**8.计算协方差**

计算两个字段间的协方差系数:df\['列1'\].cov(df\['列2'\])

计算数据表中所有字段间的协方差系数:df.cov()

![format_png 43][]

**9.计算相关性系数(**接近1为正相关，接近-1为负相关，0为不相关)

计算两个字段间的相关性系数：df\['列1'\].corr(df\['列2'\])

计算数据表中所有字段间的相关性系数：df.corr()

![format_png 44][]

发布于 2019-09-28

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