Sometimes, we need to modify a column value based upon another column value. For example, if you have two columns 'A' and 'B', and you want the value of 'B' to be Nan whenever the value of 'A' becomes 0. This can be done with the help of thepandas.DataFrame.locproperty. Note ...
import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') def modify_value(x): if x < 5: return '是' elif x < 10: return '否' else: return 'x' # 插入列 for col_num in range(4, 9): df.insert(loc=col_num, column=f'列{col_num-3}', value...
当你使用链式索引时,索引操作的顺序和类型部分地确定结果是原始对象的切片,还是切片的副本。 pandas 有 SettingWithCopyWarning,因为在切片的副本上赋值通常不是有意的,而是由于链式索引返回了一个副本而预期的是一个切片引起的错误。 如果你希望 pandas 对链式索引表达式的赋值更加信任或不信任,你可以将选项 mode.chai...
Valid values True,False,'deep' [default: True] [currently: True] display.mpl_style : bool Setting this to 'default' will modify the rcParams used by matplotlib to give plots a more pleasing visual style by default. Setting this to None/False restores the values to their initial value. [...
series.unique()->Array:返回Series对象中的唯一值数组,类似于sql中 distinct 列名,这样就不需要set(series.values.tolist())操作了。 `df["column_name"].value_counts()->Series:返回Series对象中每个取值的数量,类似于sql中group by(Series.unique())后再count() ...
5. Set and Replace values for an entire Pandas column / Series. Let’s now assume that we would like to modify the num_candidates figure for all observations in our DataFrame. That’s fairly easy to accomplish. survey_df['num_candidates'] = 25 ...
column_names = food_info.columns #获取所有的列名 dimensions = food_info.shape #获取数据的shape 1. 2. 3. 4. 5. 6. 7. Index 默认情况下,使用pandas.read_csv()读取csv文件的时候,会默认将数据的第一行当做列标签,还会为每一行添加一个行标签。我们可以使用这些标签来访问DataFrame中的数据。
values will behave as a copy. A typical example is when you are setting values in a column of a DataFrame, like: df["col"][row_indexer] = value Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ...
but whenusingCopy-on-Write (which will become thedefaultbehaviour in pandas3.0)thiswill never work toupdate the original DataFrameorSeries, because the intermediate object on which we are settingvalues will behave as a copy. A typical example is when you are setting values in a column of aDat...
def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame: employees['bonus'] = employees['salary'] * 2 return employees def modifySalaryColumn(employees: pd.DataFrame) -> pd.DataFrame: employees['salary'] = employees['salary'] * 2 ...