Pandas Get Unique Values in Column Unique is also referred to as distinct, you can get unique values in the column using pandasSeries.unique()function, since this function needs to call on the Series object, usedf['column_name']to get the unique values as a Series. Syntax: # Syntax of ...
quantile(0.75) IQR = Q3 - Q1 lower_bound = Q1 - 1.5 * IQR upper_bound = Q3 + 1.5 * IQR outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)] return outliers # 对每个指定的列查找带有异常值的记录 outliers_dict = {} for column in columns_to-check: outli...
dtype="string[pyarrow]") In [10]: ser_ad = pd.Series(data, dtype=pd.ArrowDtype(pa.string())) In [11]: ser_ad.dtype == ser_sd.dtype Out[11]: False In [12]: ser_sd.str.contains("a") Out[12]: 0 True 1 False 2 False dtype: boolean In [13]: ser_...
MultiIndex.unique([level]):返回索引中的唯一值。 MultiIndex 选择 MultiIndex.get_loc(key[, method]):获取标签或标签元组的位置作为整数,切片或布尔掩码。 MultiIndex.get_indexer(target[, method, …]):给定当前索引计算新索引的索引器和掩码。 MultiIndex.get_level_values(level):返回请求级别的标签值向量,等...
# count of each unique value in the "Gender" column print(df['Gender'].value_counts()) Output: Male 3 Female 2 Name: Gender, dtype: int64 In the above example, the pandas seriesvalue_counts()function is used to get the counts of'Male'and'Female', the distinct values in the column...
您可以使用index,columns和values属性访问数据帧的三个主要组件。columns属性的输出似乎只是列名称的序列。 从技术上讲,此列名称序列是Index对象。 函数type的输出是对象的完全限定的类名。 变量columns的对象的全限定类名称为pandas.core.indexes.base.Index。 它以包名称开头,后跟模块路径,并以类型名称结尾。 引用对...
df[col_name].unique() / df[col_name].nunique() # unique()与np.unique()效果一样,返回去重之后的结果,若原数据中包含NaN, 去重之后结果中也会有# nunique(): 返回去重之后的元素个数,可以使用参数dropna=False(默认是True)决定是否包含NaNa.nunique()# 默认dropna=Truea.nunique(dropna=False)# 不...
I want to make a bar chart for theCountrycolumn, if i try this Df.Country.value_counts().plot(kind='bar') I get this plot which is incorrect because it doesn't separate the countries. My goal is to obtain a bar chart that plots the count of each country in the column, but to ...
Assuming we have a DataFrame and need to query specific information based on certain conditions, for example, finding a column named “age” where the values are greater than a given number. We can achieve this using the Pandas’query()function. ...
converted_obj=pd.DataFrame()forcolingl_obj.columns:num_unique_values=len(gl_obj[col].unique())num_total_values=len(gl_obj[col])ifnum_unique_values/num_total_values<0.5:converted_obj.loc[:,col]=gl_obj[col].astype('category')else:converted_obj.loc[:,col]=gl_obj[col] ...