Python中缺失值一般用NaN表示,从用info()方法的结果来看,性别这一列是3 non-null object,表示性别这一列有3个非null值,而其他列有4个非null值,说明性别这一列有1个null值。 我们还可以用isnull()方法来判断哪个值是缺失值,如果是缺失值则返回True,如果不是缺失值则返回False 1.2 缺失值删除 dropna()方法默...
Data columns (total 4 columns): # Column Non-Null Count Dtype --- --- --- --- 0 animal 10 non-null object 1 age 8 non-null float64 2 visits 10 non-null int64 3 priority 10 non-null object dtypes: float64(1), int64(1), object(2) memory usage: 400.0+ bytes (2)df.describe...
Data columns (total 2 columns): # Column Non-Null Count Dtype --- --- --- --- 0 date 204 non-null datetime64[ns] 1 value 204 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 3.3 KB """ 如果是单个时间序列的数据,最好将日期列作为数据集的索引。 Numpy也有...
importmatplotlib.pyplotasplt# 统计空值和非空值的个数null_count=is_null.sum().sum()non_null_count=(~is_null).sum().sum()# 绘制饼状图labels=['Null','Non-Null']sizes=[null_count,non_null_count]explode=(0.1,0)plt.pie(sizes,explode=explode,labels=labels,autopct='%1.1f%%',shadow=True...
# Calculate the non-null observation count for each columnobs_count = df.apply(lambdax: x.notnull().sum()) # Calculate observation count for each pair of columnsobs_matrix = pd.DataFrame(index=corr_matrix.columns, columns=corr_matrix.columns)f...
3 d 6 non-null object dtypes: bool(1), float64(1), int64(1), object(1) memory usage: 278.0+ bytes 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 2、转换数值类型 数值类型包括int和float。 转换数据类型比较通用的方法可以用astype进行转换。
When trying to save a Pandas dataframe with a nested type (list within list, list within dict) using pyarrow engine, the following error is encountered ArrowInvalid: ('cannot mix list and non-list, non-null values', 'Conversion failed fo...
1 col_1 10 non-null int64 2 col_2 10 non-null int64 dtypes: int64(3) memory usage: 320.0+ bytes df1=df.set_index['colum_name'] 将某列设置为新索引 原索引还在 按照索引读取 df[0:2] >>> df 城市 人口 GDP 2 1 2 3 1 2 3 4 ...
1 PJMW_MW 143206 non-null float64 dtypes: float64(1), object(1) memory usage: 2.2+ MB None 时间列(时间戳)的处理 默认读取的时间列为字符形式,我们可以通过pandas的describe函数来进行统计,首先我们对原始时间列进行统计。 print(df_1['Datetime'].describe()) ...
# Column Non-Null Count Dtype --- --- --- --- 0satisfaction_level14999non-nullfloat64 1last_evaluation14999non-nullfloat64 2number_project14999non-nullint64 3average_montly_hours14999non-nullint64 4time_spend_company14999non-nullint64 5Work_accident14999non-null...