pandas （一）
1 Pandas序列Series1.1 根据列表生成序列 Series
X=;X
weight=;weight
sex=['女','男','男','女','男'];sex
S1=pd.Series(X);S1
S2=pd.Series(weight);S2
S3=pd.Series(sex);S3https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926210350249-1053351654.png
1.2 序列合并concat
pd.concat(,axis=0) # 0维，附加到序列后面
pd.concat(,axis=1) # 1维，附加到新的一列https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926210317825-1559673423.png
1.3 序列切片
S1
S3https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926210753303-127894205.png
2 Pandas数据框DataFrame
2.1 空数据框DataFrame
pd.DataFrame() 2.2 根据列表创建数据框columns index
pd.DataFrame(X)
# 创建一个列名为`weight`，索引为`A` `B` `C` `D` `E`的数据框
pd.DataFrame(weight,columns=['weight'],index=['A','B','C','D','E'])https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926211922697-709736236.png
2.3 根据字典创建数据框
df1=pd.DataFrame({'S1':S1,'S2':S2,'S3':S3});df1https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926212341414-2109807312.png
# 索引来自列表`X`
df2=pd.DataFrame({'sex':sex,'weight':weight},index=X);df2https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926212523992-498257299.png
2.4 增加数据框列
df2['weight2']=df2['weight']**2;df2https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926213524308-1788562806.png
2.5 删除数据框列del
del df2['weight2'];df2https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926213746056-142690846.png
2.6 缺失值处理 isnull() isnull().sum() dropna()
df3=pd.DataFrame({'S2':S2,'S3':S3},index=S1);df3
df3.isnull() # 缺失值则返回 True，否则返回 Falsehttps://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926214126920-414069514.png
df3.isnull().sum() # 返回每列中包含的缺失值个数https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926214321385-1838392180.png
df3.dropna() # 直接删除含有缺失值的行，多变量谨慎使用 不改变df3https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926214620791-1670755106.png
2.7 数据框排序
df3.sort_index() # 按index排序
df3.sort_values(by='S3') # 按S3列值排序https://img2022.cnblogs.com/blog/2185034/202209/2185034-20220926214937610-1562249503.png
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