Pandas 与 CSV

import pandas as pd

DataFrame 与 CSV 相互转换

#读取 CSV 文件

df = pd.read_csv('./nba.csv')
print(df)               # 此时仅显示前五行和后五行
print(df.to_string())   # 此方法会显示表中所有行

# 将 DataFrame 保存为 CSV 文件

gradeList = {
    'Students'  : ['Alice', 'Bob', 'Curt', 'David', 'Eve'],
    'Chinese'   : [90, 89, 68, 88, 69],
    'Math'      : [89, 70, 89, 99, 100],
    'English'   : [89, 67, 78, 89, 67]
}
df = pd.DataFrame(data=gradeList)

df.to_csv('./gradeList.csv')        # 将 DataFrame 保存为 CSV 文件

数据处理

df = pd.read_csv('nba.csv')

head(n) 读取 DataFrame 的前 n 行

# head(n) 读取 DataFrame 的前 n 行
print(df.head(3))

Name Team Number Position Age Height Weight
0 Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0
1 Jae Crowder Boston Celtics 99.0 SF 25.0 6-6 235.0
2 John Holland Boston Celtics 30.0 SG 27.0 6-5 205.0

College Salary
0 Texas 7730337.0
1 Marquette 6796117.0
2 Boston University NaN

tail(n) 读取 DataFrame 的尾 n 行

# tail(n) 读取 DataFrame 的尾 n 行
print(df.tail(3))

Name Team Number Position Age Height Weight College
455 Tibor Pleiss Utah Jazz 21.0 C 26.0 7-3 256.0 NaN
456 Jeff Withey Utah Jazz 24.0 C 26.0 7-0 231.0 Kansas
457 NaN NaN NaN NaN NaN NaN NaN NaN

Salary
455 2900000.0
456 947276.0
457 NaN

info() 返回表格的一些基本信息

# info() 返回表格的一些基本信息
print(df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 458 entries, 0 to 457
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Name 457 non-null object
1 Team 457 non-null object
2 Number 457 non-null float64
3 Position 457 non-null object
4 Age 457 non-null float64
5 Height 457 non-null object
6 Weight 457 non-null float64
7 College 373 non-null object
8 Salary 446 non-null float64
dtypes: float64(4), object(5)
memory usage: 32.3+ KB
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