注释和空行 2017/12/27
目录:
第1部分:csv文本文件读写
pandas 读csv文件read_csv(1.文本读写概要)https://mp.csdn.net/postedit/85289371
pandas 读csv文件read_csv(2.read_csv参数介绍)https://mp.csdn.net/postedit/85289928
pandas 读csv文件read_csv(3.dtypes指定列数据类型)https://mp.csdn.net/postedit/85290575
pandas 读csv文件read_csv(4.to_csv文本数据写)https://mp.csdn.net/postedit/85290962
pandas 读csv文件read_csv(5.文本数据读写实例)https://mp.csdn.net/postedit/85291123
pandas 读csv文件read_csv(6.命名和使用列)https://mp.csdn.net/postedit/85291430
pandas 读csv文件read_csv(7.索引)https://mp.csdn.net/postedit/85291658
pandas 读csv文件read_csv(8.方言和分隔符)https://mp.csdn.net/postedit/85291994
pandas 读csv文件read_csv(9.浮点转换和NA值)https://mp.csdn.net/postedit/85292391
pandas 读csv文件read_csv(10.注释和空行)https://mp.csdn.net/postedit/85292609
pandas 读csv文件read_csv(11.日期时间处理) https://mp.csdn.net/postedit/85292925
pandas 读csv文件read_csv(12.迭代和块)https://mp.csdn.net/postedit/85293639
pandas 读csv文件read_csv(13.read_fwf读固定宽度数据)https://mp.csdn.net/postedit/85294010
第2部分:
pandas hdf文件读写简要https://mp.csdn.net/postedit/85294299
pandas excel读写简要https://mp.csdn.net/postedit/85294545
第3部分:
python中csv模块用法tcy https://mp.csdn.net/postedit/85228189
pandas读csv文件read_csv错误解决办法7种https://mp.csdn.net/postedit/85228808
pandas to_string用法https://mp.csdn.net/postedit/85294935
实例:
# 实例1:忽略行注释和空行
# 如果comment指定了参数,则将忽略完全注释的行。默认情况下,也会忽略完全空行。
data = '\na,b,c\n \n # commented line\n1,2,3\n\n4,5,6'
pd.read_csv(StringIO(data), comment='#') #忽略完全注释,忽略空行
pd.read_csv(StringIO(data), skip_blank_lines=False)#忽略完全注释,空行用nan值填写
警告:
# 忽略的行的存在可能会产生涉及行号的含糊不清;
# 该参数header使用行号(忽略注释/空行),同时skiprows使用行号(包括注释/空行)
实例2:
data = '#comment\na,b,c\nA,B,C\n1,2,3'
pd.read_csv(StringIO(data), comment='#', header=1)
# A B C
# 0 1 2 3
实例3:
# 如果同时header和skiprows指定,header对应skiprows相对的结束的位置
data = '# empty\n' \
'# second empty line\n' \
'# third empty line\n' \
'X,Y,Z\n' \
'1,2,3\n' \
'A,B,C\n' \
'1,2.,4.\n' \
'5.,NaN,10.0'
print(data)
pd.read_csv(StringIO(data), comment='#', skiprows=4, header=1)
# 输出:
A B C
0 1.0 2.0 4.0
1 5.0 NaN 10.0
实例4:
data="ID,level,category\n" \
"Patient1,123000,x # really unpleasant\n" \
"Patient2,23000,y # wouldn't take his medicine\n" \
"Patient3,1234018,z # awesome"# 原数据包含注释
pd.read_csv(StringIO(data))
# 输出:
ID level category
0 Patient1 123000 x # really unpleasant
1 Patient2 23000 y # wouldn't take his medicine
2 Patient3 1234018 z # awesome
pd.read_csv(StringIO(data), comment='#')#忽略注释
# 输出:
ID level category
0 Patient1 123000 x
1 Patient2 23000 y
2 Patient3 1234018 z