pandas常用操作

谁借莪１个温暖的怀抱￠ 2022-12-12 15:17 185阅读 0赞

视频来源链接: [https://www.bilibili.com/video/BV1SE411679D?p=1][https_www.bilibili.com_video_BV1SE411679D_p_1]

### 目录 ###

*  导入库
 *  读取文件
 *  查看数据表信息
 *  数据清洗
 *  数据合并
 *  数据索引
 *  数据提取
 *  数据筛选
 *  数据汇总
 *  数据统计

# 导入库 #

import numpy as np
    import pandas as pd

# 读取文件 #

# 创建数据表
    data = pd.DataFrame({
        
    		'列名':[该列的值], 
    		....})
    		
    # 保存创建的数据表, path为xxx.csv文件路径
    data.to_csv(path, index=False)
    
    # 读取csv文件, path为xxx.csv文件路径
    read_data = pd.read_csv(path)

# 查看数据表信息 #

# 查看数据维度
    read_data.shape
    
    # 查看数据基本信息（维度、列名称、数据格式、所占空间等）
    read_data.info()
    
    # 查看每一列的数据格式
    read_data.dtypes
    
    # 查看某一列格式, key = 列名
    read_data[key].dtype
    
    # 查看空值
    read_data.isnull()
    
    # 查看某一列空值
    read_data[key].isnull()
    
    # 查看某一列的唯一值
    read_data[key].unique()
    
    # 查看某一列值的分布
    read_data[key].value_counts(dropna=False)  # dropna=False表示该列有空值也打印出空值个数
    
    # 查看数据表的值
    read_data.values  # 返回值为除去列名和序号后的数据
    
    # 查看列名称
    read_data.columns
    
    # 查看前n行, 后n行数据
    read_data.head(n)  # n可以不写，默认值为5
    read_data.tail(n)

# 数据清洗 #

# 填充默认原数据表不会被修改(xxx.fillna(inplace=False))，返回值为修改后的列表; 若(xxx.fillna(inplace=True)),则原数据表会被修改。
    
    # 用0值填充空值
    read_data[key].fillna(value=0)
    
    # 用空值前或后的值填充空值
    read_data[key].fillna(method='ffill')  # 前
    read_data[key].fillna(method='bfill')  # 后
    
    # 使用均值填充
    read_data[key].fillna(read_data[key].mean(), inplace=True)
    
    # 清除某一列的字符空格
    read_data[key] = read_data[key].map(str.strip)
    
    # 大小写转换
    read_data[key] = read_data[key].str.lower()  # 转小写
    read-data[key] = read_data[key].str.upper()  # 转大写
    
    # 更改数据格式
    read_data[key].astype(dtype)  # dtype为字符串格式, 如 'str', 'int64'...
    
    # 更改列名称
    read_data.rename(columns={
        原名:修改后的名称, ...})
    
    # 删除后出现重复的值
    read_data[key].drop_duplicates()
    
    # 删除先出现的重复值
    read_data[key].drop_duplicates(keep='last)
    
    # 数据替换
    read_data[key].replace(原数据, 替换后的数据)  # 该列所有与原数据相等的值都会替换成你想要改成的数据值

# 数据合并 #

data2 = pd.DataFrame({
        
    		'列名':[该列的值], 
    		....})
    
    # 数据表合并:merge, join, concat
    # merge
    # 内合并
    data_inner = pd.merge(read_data, data2, on=key, how='inner')  # 交集, on=key表示两个表key列相同才把data2的数据加到read_data后面
    # 左合并
    data_left = pd.merge(read_data, data2, on=key, how='left')  # 将key列相同值的data2添加到read_data后面, read_data未被添加的用空值填充
    # 右合并
    data_right = pd.merge(read_data, data2, on=key, how='right')  # # 将key列相同值的read_data添加到data2前面, data2未被添加的用空值填充
    # 并集
    data_outer = pd.merge(read_data, data2, on=key, how='outer').
    
    # append, 在原来的数据表中追加一行
    data3 = pd.DataFrame({
        
    		'列名':[该列的值], 
    		....})
    result = data2.append(data3)

# 数据索引 #

# 设置索引列
    data2.set_index(key)
    # 索引列还原
    data2.reset_index(drop=True)
    
    # 按照特定列的值排序
    data2.sort_values(by=[key])
    
    # 按照索引列排序
    data2.sort_index()

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# 数据提取 #

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# 数据筛选 #

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![在这里插入图片描述][20201008154931314.png_pic_center]

# 数据汇总 #

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# 数据统计 #

随机采样  
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[https_www.bilibili.com_video_BV1SE411679D_p_1]: https://www.bilibili.com/video/BV1SE411679D?p=1
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