fillna(value) # 填充缺失值 # 数据转换和处理 df.groupby(column_name).mean() # 按列名分组并计算均值 df[column_name].apply(function) # 对某一列应用自定义函数 数据可视化 import matplotlib.pyplot as plt # 绘制柱状图 df[column_name].plot
HDFStore.append(key, value,format=None, axes=None, index=True, append=True, complib=None, complevel=None, columns=None, min_itemsize=None, nan_rep=None, chunksize=None, expectedrows=None, dropna=None, data_columns=None, encoding=None, errors='strict') 追加到文件中的表。 节点必须已经存在...
max() # Index of the lowest value df.idxmin() # Index of the highest value df.idxmax() # Statistical summary of the data frame, with quartiles, median, etc. df.describe() # Average values df.mean() # Median values df.median() # Correlation between columns df.corr() # To get ...
修改单个值:df.at[row_index, 'column_name'] = new_value 修改多个值:df.loc[row_indexer, 'column_name'] = new_value 或df.iloc[row_indexer, column_indexer] = new_value df = pd.DataFrame({'姓名':['abao','XQ','翔光','勍哥'],'科目':['语文','数学','英语','英语'],'分数':...
df = pd.DataFrame({'column_name': ['value1', 'value2', 'value3']}) 使用条件判断来筛选包含"-"的值,并执行相应的操作: 代码语言:txt 复制 df['column_name'] = df['column_name'].apply(lambda x: x if '-' not in x else 'new_value') 上述代码中,使用了apply函数和lambda...
Max: df.max() – highest value in each column Min: df.min() – lowest value in each column Count: df.count() – number of non-null values in each DataFrame column Describe: df.describe() – Summary statistics for numerical columns apply those methods in our Product_ReviewDataFrame Droppi...
value =None :希望填充的新数值 inplace = False) 8、生成哑变量:pd.get_dummies(df1.var2,prefix='pre') pd.get_dummies(df1,prefix='pre',columns=['var2']) 参数说明:pd.get dummies( data :希望转换的数据框/变量列 prefix = None :哑变量名称前缀 ...
of the lowest valuedf.idxmin()# Index of the highest valuedf.idxmax()# Statistical summary of the data frame, with quartiles, median, etc.df.describe()# Average valuesdf.mean()# Median valuesdf.median()# Correlation between columnsdf.corr()# To get these values for only one column, ...
29. Delete Rows by Column ValueWrite a Pandas program to delete DataFrame row(s) based on given column value. Sample data: Original DataFrame col1 col2 col3 0 1 4 7 1 4 5 8 2 3 6 9 3 4 7 0 4 5 8 1 New DataFrame col1 col2 col3 0 1 4 7 2 3 6 9 3 4 7 0 4 ...
# Sum of values in a data frame df.sum() # Lowest value of a data frame df.min() # Highest value df.max() # Index of the lowest value df.idxmin() # Index of the highest value df.idxmax() # Statistical summary of the data frame, with quartiles, median, etc. ...