Table of contents
1. Read the excel file
(1) Grammar
import pandas as pd
data = pd.read_excel(io,
sheet_name=0,
header=0,
names=None,
index_col=None,
usecols=None,
squeeze=False,
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skiprows=None,
nrows=None,
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
parse_dates=False,
date_parser=None,
thousands=None,
comment=None,
skipfooter=0,
convert_float=True,
mangle_dupe_cols=True)Description of Common Parameters
io: The name of the excel file to read, such as r'./vote.excel'.
sheet_name: the name of the sheet in the excel file.
header: Which row is set as the column index, the default is the first row, ie header = 0.
names: column index names.
index_col: Which column to use as the row index, starting from 0 by default.
usecols: Which columns in the table to read must be positional indexes.
skiprows: Skip the first few lines to read the file, starting from 0 by default.
nrows: How many rows of data to read.
(2) Examples
Read six rows of data in the specified column of the classification sheet in the file.
import pandas as pd
data = pd.read_excel(r'.\data\sep_word - 1.0.xlsx',sheet_name= '分类',header= 0,nrows=6,usecols=[0,1,3,5])
dataThe output is
Explanation: usecols=[0,1,3,5] refers to columns 1,2,4,6.
2. Read the cvs file
(1) Grammar
import pandas as pd
data = pd.read_cvs(filepath_or_buffer: FilePathOrBuffer,
sep=",",
delimiter=None,
# Column and Index Locations and Names
header="infer",
names=None,
index_col=None,
usecols=None,
squeeze=False,
prefix=None,
mangle_dupe_cols=True,
# General Parsing Configuration
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
# NA and Missing Data Handling
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
# Datetime Handling
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
# Iteration
iterator=False,
chunksize=None,
# Quoting, Compression, and File Format
compression="infer",
thousands=None,
decimal: str = ".",
lineterminator=None,
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
# Error Handling
error_bad_lines=True,
warn_bad_lines=True,
# Internal
delim_whitespace=False,
low_memory=_c_parser_defaults["low_memory"],
memory_map=False,
float_precision=None)Parameter Description:
The csv file is a comma-delimited file. The reading parameters are basically similar to excel. The file is csv in gbk format. If the encoding parameter is not set, an error will be reported.
encoding: The default is 'utf-8', and there are also Chinese encodings 'gbk', 'gb18030', 'gb2312'.
As far as the Chinese characters we care about, the representation ranges of the three encoding methods are:
GB18030 > GBK > GB2312
, that is, GBK is a superset of GB2312, and GB1803 is a superset of GBK.
Generally, you can directly use encoding =GB18030 to read Chinese text
(2) Examples
Direct reading does not set the encoding method, the storage method may exist in gbk format, and Chinese characters will be garbled.
import pandas as pd
data = pd.read_csv(r'.\python\python数据分析\word.csv')
dataThe output is:
Generally, encoding='utf-8' can solve a lot of garbled encoding problems, but errors are still reported.
显示：UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc0 in position 37: invalid start byte
It may be because Chinese is stored in gbk format, and utf-8 still cannot recognize some encodings, so you can try to use gbk, and you need to set the encoding method encoding='gbk'.
import pandas as pd
data = pd.read_csv(r'.\python\python数据分析\word.csv',encoding='gbk')
dataThe output is:
Just want to view the first 10 rows of data with the head function.
data.head(10) #查看前十行数据
data.head() #默认显示前5行数据
3. Read the txt file
(1) Grammar
import pandas as pd
data = pd.read_table(filepath_or_buffer: FilePathOrBuffer,
sep="\t",
delimiter=None,
# Column and Index Locations and Names
header="infer",
names=None,
index_col=None,
usecols=None,
squeeze=False,
prefix=None,
mangle_dupe_cols=True,
# General Parsing Configuration
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
# NA and Missing Data Handling
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
# Datetime Handling
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
# Iteration
iterator=False,
chunksize=None,
# Quoting, Compression, and File Format
compression="infer",
thousands=None,
decimal: str = ".",
lineterminator=None,
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
# Error Handling
error_bad_lines=True,
warn_bad_lines=True,
# Internal
delim_whitespace=False,
low_memory=_c_parser_defaults["low_memory"],
memory_map=False,
float_precision=None)Parameter Description:
The txt file is a file with the tab character \t as the delimiter. The parameters are basically similar to those of excel and csv. The difference is that sep must be specified.
sep: Defaults to '\t'.
(2) Examples
Read campaign documents.
data = pd.read_table(r'.\python\python数据分析\智能空调项目\python_study\vote.txt')
dataThe output is:
4. Write to file
Excel, csv, and txt are basically similar in the way of writing files, and are written in the way of to_xx() of pandas.
(1) Grammar
#写入excel文件
to_excel(
self,
excel_writer,
sheet_name="Sheet1",
na_rep="",
float_format=None,
columns=None,
header=True,
index=True,
index_label=None,
startrow=0,
startcol=0,
engine=None,
merge_cells=True,
encoding=None,
inf_rep="inf",
verbose=True,
freeze_panes=None
) Description of common parameters:
index: Whether to keep the row index, the default is True to keep, False means not to keep.
columns: Specify the required columns by column index.
sheet_name: sheet name, default is 'sheet1'.
encoding: encoding format, utf-8 or gbk.
na_rep: missing value filling, can be specified as 0.
index_label: row index label.
header: The default is True, False has no column index, if you need to change the column name, then header = ["Column 1", "Column 2", "Column 3"]
(2) Examples
Write txt document as xlsx file.
import pandas as pd #导入pandas库
#读入txt文档
data = pd.read_table(r'.\python\python数据分析\智能空调项目\python_study\vote.txt',sep='\t')
#写入excel文档
data.to_excel(r'./vote.xlsx',sheet_name='vote',na_rep='')Reference article:
The road to python learning - pandas read and write files - Zhihu (zhihu.com)