iloc 访问单个元素 value = df.iloc[0, 0] # 返回 1 # 使用 iloc 访问多行多列 subset = df.iloc[[0, 2], [0, 2]] # 使用 iloc 访问切片 slice_df = df.iloc[0:2, 1:3] # 使用负索引访问最后几行 last_rows = df.iloc[-2:] # 使用步长访问数据 every_other_row = df.iloc[::2]...
(列名称,控制header) index_label=None, #设置列索引名,默认为None,如果header和index都设置为Ture,这个没必要管 startrow=4, #设置写入的数据从第几行开始写入,默认为0,比如这里设置为4,那么元数据第一行数据将出现在第5行,上边四行空出 startcol=2, #设置写入的数据从第几列开始写入,默认为0,比如这里...
1. 导入模块 import pandas as pd import numpy as np 2. 读取数据和保存数据 2.1 从CSV文件读取数据,编码'gbk'2.2 读取前6行,当数据量比较大时，可以只读取前n行 2.3 第一列作为行索引,忽略列索引 2.4 读取时忽略第1/3/5行和最后两行 2.5 从限定分隔符（','）的文件或文本读取数据 2.6 保...
5)) In [37]: arr[arr < .9] = 0 In [38]: sp_arr = csr_matrix(arr) In [39]: sp_arr Out[39]: <1000x5 sparse matrix of type '<class 'numpy.float64'>' with 517 stored elements in Compressed Sparse Row format> In [40]: sdf = pd.DataFrame.sparse.from_sp...
The last row without any NaN is taken (or the last row without DataFrame.shift([periods, freq, axis]) Shift index by desired number of periods with an optional time freq DataFrame.first_valid_index() Return label for first non-NA/null value ...
df_last[(df_last['语文']>=80) | (df_last['数学']>=90)] # 6.8 输出成绩100的行和列号 row, col = np.where(df_last.values ==100) # 6.9 增加一列“省份-城市” df_last['省份-城市'] = df_last['省份'] +'-'+ df_last['城市'] ...
t in tmp[n:]:if num==t[idx]:lst[-1]=telse:breakcase 'all':for t in tmp[n:]:if num==t[idx]:lst.append(t)else:breakcase _:raise ValueError('keep must be either "first", "last" or "all"')res = dict()for row in lst:for col,key in enumerate(self.__dic.keys()):tmp...
at Get or set the value of the item with the specified label axes Returns the labels of the rows and the columns of the DataFrame bfill() Replaces NULL values with the value from the next row bool() Returns the Boolean value of the DataFrame columns Returns the column labels of the Dat...
lastRow = ws.Cells(ws.Rows.Count, 1).End(xlUp).Row For col = 2 To lastCol For Each cell In ws.Range(ws.Cells(2, col), ws.Cells(lastRow, col)) If IsEmpty(cell.Value) Then cell.Value = 0 Next cell With ws.Range(ws.Cells(2, col), ws.Cells(lastRow, col)) ...
df_last[(df_last['语文']>=80) & (df_last['数学']>=90)] # 6.7 语文成绩80及以上或数学成绩90分及以上 df_last[(df_last['语文']>=80) | (df_last['数学']>=90)] # 6.8 输出成绩100的行和列号 row, col = np.where(df_last.values == 100) ...