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()

.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {

    text-align: left;

}

.dataframe tbody tr th {

    vertical-align: top;

}

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	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()

.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {

    text-align: left;

}

.dataframe tbody tr th {

    vertical-align: top;

}

	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()

.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {

    text-align: left;

}

.dataframe tbody tr th {

    vertical-align: top;

}

		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()

.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {

    text-align: left;

}

.dataframe tbody tr th {

    vertical-align: top;

}

	0	1	2	3	4	5	6	8	9	10	11
0	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
1	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
2	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S
3	6	0	3	Moran, Mr. James	male	NaN	0	330877	8.4583	NaN	Q
4	7	0	1	McCarthy, Mr. Timothy J	male	54.0	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

.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {

    text-align: left;

}

.dataframe tbody tr th {

    vertical-align: top;

}

	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)