In [32]: df = pd.DataFrame( ...: np.random.randn(3, 2), columns=[" Column A ", " Column B "], index=range(3) ...: ) ...: In [33]: df Out[33]: Column A Column B 0 0.469112 -0.282863 1 -1.509059 -1.135632 2 1.212112 -0.173215 由于df.columns是一个 Index 对象,我们...
.: In [33]: df Out[33]: Column A Column B 0 0.469112 -0.282863 1 -1.509059 -1.135632 2 1.212112 -0.173215 分割和替换String Split可以将一个String切分成一个数组。 代码语言:javascript 复制 In [38]: s2 = pd.Series(['a_b_c', 'c_d_e', np.nan, 'f_g_h'], dtype="string") In...
returnpd.Series(['🟥'ifitem == row_data.minelse'🟩'ifitem == row_data.maxelse'⬜'foriteminrow_data]) defget_conditional_table_column(data, bins=3, emoji='circle'): tmp = data.copy forcolumnindata.columns: ifpd.api.types.is_numeric_dtype(data[column]): row_data_emoji = get...
if pd.api.types.is_numeric_dtype(datacolumn): row_data_emoji = get_percentiles(datacolumn, bins, emoji).astype(str) tmpcolumn= datacolumn.astype(str) + ' ' + row_data_emoji return tmp def get_conditional_table_row(data, bins=3, emoji='circle'): response_values =\] column\_str = \...
items(): print(f"Outliers in '{column}':") print(outliers) print("\n") 'AveRooms'列中的异常值 | 用于异常值检查的截断输出 3.5 验证数值范围 对于数值特征,一项重要的检查是验证范围。这可以确保特征的所有观测值都在预期范围内。 以下代码将验证MedInc值是否在预期范围内,并识别出不符合...
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, ...
df = pd.DataFrame(a, dtype='float')#示例1df = pd.DataFrame(data=d, dtype=np.int8)#示例2df = pd.read_csv("somefile.csv", dtype = {'column_name': str}) 对于单列或者Series 下面是一个字符串Seriess的例子,它的dtype为object:
income.set_index("Index",inplace = True)income.head()#Note that the indices have changed and Index column is now no more a columnincome.columnsincome.reset_index(inplace = True) # use the by default indices.income.head() 行列删除 ...
extract Use a regular expression with groups to extract one or more strings from a Series of strings; the result will be a DataFrame with one column per group endswith Equivalent to x.endswith(pattern) for each element startswith Equivalent to x.startswith(pattern) for each element findall ...
>>> pd.read_csv('data.csv', usecols=['column_name1','column_name2'])#To set a column as the index column >>> pd.read_csv('data.csv',index_col='Name') 1. 2. 3. 4. 类似函数:read_(is the type of file you want to read, eg. read_json, read_excel) ...