Table 1 shows our example DataFrame. As you can see, it contains six rows and three columns. Multiple cells of our DataFrame contain NaN values (i.e.missing data). In the following examples, I’ll explain how to
# Remove the nan and fill the empty string df2 = df.Courses.replace(np.nan,'',regex = True) # Remove the nan and fill some values df2 = df.Courses.replace(np.nan,'value',regex = True) Now, let’s create a DataFrame with a few rows and columns and execute some examples, and va...
5]),columns=['a',np.nan,'b',np.nan,'c'])df'''a NaN b NaN c0 1.0 1....
(include=['int']).sum(1)df['total'] = df.loc[:,'Q1':'Q4'].apply(lambda x: sum(x), axis='columns') df.loc[:, 'Q10'] = '我是新来的' # 也可以 # 增加一列并赋值,不满足条件的为NaN df.loc[df.num >= 60, '成绩'] = '合格' df.loc[df.num < 60, '成绩'] = '不...
read_excel可以通过将列列表传递给index_col和将行列表传递给header来读取MultiIndex索引。如果index或columns具有序列化级别名称,也可以通过指定构成级别的行/列来读取这些级别。 例如,要读取没有名称的MultiIndex索引: In [424]: df = pd.DataFrame(...: {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]...
columns:需要聚合的列索引,可以是列表,会生成多级索引 aggfunc:默认为平均值,也可以选择一个函数列表,计算多个聚合数据 dropna:True 忽略nan值 fill_value:缺失数据填充 margins:True添加合计行和合计列数据(all),False则不添加 margins_name:默认合计行、合计列名称为all 四、跨表组合 pd.crosstab(index, columns,...
Python program to remove duplicate columns in Pandas DataFrame # Importing pandas packageimportpandasaspd# Defining two DataFramesdf=pd.DataFrame( data={"Parle": ["Frooti","Krack-jack","Hide&seek","Frooti"],"Nestle": ["Maggie","Kitkat","EveryDay","Crunch"],"Dabur": ["Chawanprash","Hon...
The above method will ignore the NaN values from title column. We can also remove all the rows which have NaN values... How To Drop NA Values Using Pandas DropNa df1 = df.dropna() In [46]: df1.size Out[46]: 16632 As we can see above dropna() will remove all the rows where...
Pandas add column with value based on condition based on other columns Drop row if two columns are NaN Count and Sort with Pandas How to delete all rows in a dataframe? Create an empty MultiIndex Pandas convert month int to month name ...
columns=df['日期'].dt.month, aggfunc=np.sum) print(pivot) 四、实战案例:销售数据分析 4.1 数据加载与探索 sales_data = pd.read_csv('sales.csv', parse_dates=['order_date'], dtype={'product_id': 'category'}) print(sales_data.info()) ...