df.apply(lambda row: row[df['Courses'].isin(['Spark','PySpark'])]) df.dropna() Delete Rows Based on Inverse of Condition Finally, the negation (~) operation can be used to drop all rows except those that meet a certain condition. This is a very handy feature in Pandas for quickly ...
Pandas Drop the First Row using iloc[] To drop the first row usingiloc[], you can specify the index range from the second row onwards. For instance, useDataFrame.iloc[1:]to select all rows from index position 1 (inclusive) onwards. By specifying[1:], you’re effectively excluding the ...
删除满足条件的行:使用drop()方法删除满足条件的行。需要设置axis=0参数表示按行删除。 代码语言:txt 复制 # 删除满足条件的行 df = df.drop(df[condition].index, axis=0) 查看结果:可以使用head()方法查看删除后的DataFrame的前几行,以确保满足条件的行已被删除。 代码语言:txt 复制 # 查看删除后的结果 p...
代码语言:javascript 复制 In [122]: s = pd.Series([0, 1, 2, 3, 4, 5]) # When no arguments are passed, returns 1 row. In [123]: s.sample() Out[123]: 4 4 dtype: int64 # One may specify either a number of rows: In [124]: s.sample(n=3) Out[124]: 0 0 4 4 1 1 ...
row_number:行号。 column_number:列号。 使用实例: # 选择第1行和第1列的单个元素 print(df.iat[1, 1]) 输出结果:5 5.[]操作符 用处:基于列标签选择列,或者基于布尔条件过滤行。 语法规范: DataFrame['column_label'] DataFrame[boolean_condition] ...
df.apply(lambda row: '|'.join(filter(pd.notna, [row['source_1'], row['source_2']])), axis=1) sort_values(by=multiple columns) sort_values可以不止一个column,可以多个。 df.sort_values(by=['name', 'number']) 还可以指定ascending=[False, True] ...
df.columns=df.loc[first_row] #更改当前df的列索引名称 df.rename(columns =lambda x:str(x).strip("\r\n\t ."),inplace=True) #去掉列名首位的空白字符 df=df.loc[first_row+1:] df_dst=pd.merge(df_dst,df[["姓名","年级","合计"]],how="outer",on="姓名",suffixes=("",sht_name))...
diff() Calculate the difference between a value and the value of the same column in the previous row div() Divides the values of a DataFrame with the specified value(s) dot() Multiplies the values of a DataFrame with values from another array-like object, and add the result drop() Drop...
pandas 提供了用于操作Series和DataFrame的方法,以改变数据的表示形式,以便进行进一步的数据处理或数据汇总。 pivot()和pivot_table():在一个或多个离散类别中对唯一值进行分组。 stack()和unstack():分别将列或行级别的数据透视到相反的轴上。 melt()和wide_to_long():将宽格式的DataFrame转换为长格式。
print(df.drop("A", axis=1)) people = pd.read_excel("test1.xlsx") columns_name = ["A", "B", "C"] # 对行求平均值,总和 row_mean = people[columns_name].mean(axis=1) row_sum = people[columns_name].sum(axis=1) total = "总分" ...