Python program to select rows whose column value is null / None / nan # Importing pandas packageimportpandasaspd# Importing numpy packageimportnumpyasnp# Creating a dictionaryd={'A':[1,2,3],'B':[4,np.nan,5],'C':[np.nan,6,7] }# Creating DataFramedf=pd.DataFrame(d)# Display data...
# Select the best split point for a datasetdef get_split(dataset):class_values = list(set(row[-1] for row in dataset))b_index, b_value, b_score, b_groups = 999, 999, 999, Nonefor index in range(len(dataset[0])-1):for row in dataset:groups = test_split(index, row[index],...
df.Amplitude, lw=1.5, label = 'Actual Value') ax1.plot(df.index, df.FirstOrderDiff, ...
# impute the missing values and create the missing value indicator variables for each non-numeric column.df_non_numeric=df.select_dtypes(exclude=[np.number])non_numeric_cols=df_non_numeric.columns.valuesforcol in non_numeric_cols:missing=df[col].isnull()num_missing=np.sum(missing)ifnum_miss...
[index, name])Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple.DataFrame.lookup(row_labels, col_labels)Label-based “fancy indexing” function for DataFrame.DataFrame.pop(item)返回删除的项目DataFrame.tail([n])返回最后n行DataFrame.xs(key[, axis, level...
Selection.CurrentRegion.Select() Selection.Copy() Selection.PasteSpecial(Paste=xlPasteValues, Operation=xlNone, SkipBlanks=False, Transpose=False) VBA中默认你操作的当前worksheet,所以可以直接使用Range对象,Selection对象,但是python中不能直接这样简写,改造和简化后应该是: ...
SELECT sr_customer_sk, -- return order ratio count(distinct(sr_ticket_number)) as returns_count, -- return ss_item_sk ratio COUNT(sr_item_sk) as returns_items, -- return monetary amount ratio SUM( sr_return_amt ) AS returns_money ...
SELECT column_name, function(column_name) FROM table_name WHERE column_name operator value GROUP BY column_name; mysql> select *fromstudent;+---+---+---+---+ | id | name | age | register_date | +---+---+---+---+ | 1 |...
# Remove last rowfortotal column attribute medal_noc=medal_noc.drop([medal_noc.shape[0]-1],axis=0)medal_noc#2General champion medal_noc_year=medal_noc.loc[medal_noc.groupby('Year')['All'].idxmax()].sort_values('Year')medal_noc_year ...
print(df['Department'].value_counts()) # 分类计数 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 1.2 数据清洗与转换 数据清洗是数据分析的关键步骤: # 处理缺失值 df.loc[2, 'Age'] = np.nan df['Age'] = df['Age'].fillna(df['Age'].mean()) ...