pd.read_csv() 、to_csv() 之 常用参数
本文简单介绍一下read_csv()和 to_csv()的参数，最常用的拿出来讲，较少用的请转到官方文档看。
一.pd.read_csv()
作用：将csv文件读入并转化为数据框形式。
pd.read_csv(filepath_or_buffer, sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal=b'.', lineterminator=None, quotechar='"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=False, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, skip_footer=0, doublequote=True, delim_whitespace=False, as_recarray=False, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, memory_map=False, float_precision=None)
好多参数呀！
下面来看常用参数：
1.filepath_or_buffer:（这是唯一一个必须有的参数，其它都是按需求选用的）
文件所在处的路径
2.sep：
指定分隔符，默认为逗号','
3.delimiter : str, default None
定界符，备选分隔符（如果指定该参数，则sep参数失效）
4.header：int or list of ints, default ‘infer’
指定哪一行作为表头。默认设置为0（即第一行作为表头），如果没有表头的话，要修改参数，设置header=None
5.names：
指定列的名称，用列表表示。一般我们没有表头，即header=None时，这个用来添加列名就很有用啦！
6.index_col:
指定哪一列数据作为行索引，可以是一列，也可以多列。多列的话，会看到一个分层索引
7.prefix:
给列名添加前缀。如prefix="x",会出来"x1"、"x2"、"x3"酱纸
8.nrows : int, default None
需要读取的行数（从文件头开始算起）
9.encoding:
乱码的时候用这个就是了，官网文档看看用哪个：
https://docs.python.org/3/library/codecs.html#standard-encodings
10.skiprows : list-like or integer, default None
需要忽略的行数（从文件开始处算起），或需要跳过的行号列表（从0开始）。
下面是举栗子时间：
import pandas as pd
data = pd.read_csv(r"G:\data\Kaggle\Titanic\train.csv")
data.head()
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
data1 = pd.read_csv(r"G:\data\Kaggle\Titanic\train.csv",header=None) #可以看到表头都直接当作数据在用了
data1.head()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |
| 1 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 1 | 0 | A/5 21171 | 7.25 | NaN | S |
| 2 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 3 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 0 | 0 | STON/O2. 3101282 | 7.925 | NaN | S |
| 4 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 1 | 0 | 113803 | 53.1 | C123 | S |
data2 = pd.read_csv(r"G:\data\Kaggle\Titanic\train.csv",index_col=["Survived","Sex"])
data2.head()
| PassengerId | Pclass | Name | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Survived | Sex | ||||||||||
| 0 | male | 1 | 3 | Braund, Mr. Owen Harris | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | female | 2 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| female | 3 | 3 | Heikkinen, Miss. Laina | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S | |
| female | 4 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S | |
| 0 | male | 5 | 3 | Allen, Mr. William Henry | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
data3 = pd.read_csv(r"G:\data\Kaggle\Titanic\train.csv", skiprows=3, header=None) #包括表头的前三行被跳过了
data3.head()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 1 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 2 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
| 3 | 6 | 0 | 3 | Moran, Mr. James | male | NaN | 0 | 0 | 330877 | 8.4583 | NaN | Q |
| 4 | 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54.0 | 0 | 0 | 17463 | 51.8625 | E46 | S |
二.pd.to_csv()
作用：将数据框写入本地电脑，保存起来
先了解一下当前工作路径
import os
father_path = os.getcwd()
father_path
'C:\\Users\\acerpc'
to_csv(path_or_buf,sep,na_rep,columns,header,index)
参数解析：
1.path_or_buf：字符串，放文件名、相对路径、文件流等；
2.sep：字符串，分隔符，跟read_csv()的一个意思
3.na_rep：字符串，将NaN转换为特定值
4.columns：列表，指定哪些列写进去
5.header：默认header=0，如果没有表头，设置header=None，表示我没有表头呀！
6.index：关于索引的，默认True,写入索引
举栗子时间到：
import numpy as np
df = pd.DataFrame({"a":[1,2,3],
"b":[6,np.nan,6],
"c":[3,4,np.nan]})
df
| a | b | c | |
|---|---|---|---|
| 0 | 1 | 6.0 | 3.0 |
| 1 | 2 | NaN | 4.0 |
| 2 | 3 | 6.0 | NaN |
path1 = father_path + r'\df1.csv'
df.to_csv(path1)
path2 = father_path + r'\df2.csv'
df.to_csv(path2,header=None)
path3 = father_path + r'\df3.csv'
df.to_csv(path3, columns=["a","c"],index=False)
path4 = father_path + r'\df4.csv'
df.to_csv(path4, na_rep=0)
