Pandas使用DataFrame进行数据分析比赛进阶之路(一)

qinjianhuang 2020-11-13 05:22:59
pandas 使用 进行 dataframe 行数


这篇文章中使用的数据集是一个足球球员各项技能及其身价的csv表,包含了60多个字段。数据集下载链接:数据集

1、DataFrame.info()

这个函数可以输出读入表格的一些具体信息。这对于加快数据预处理非常有帮助。

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('dataset/soccer/train.csv')
print(data.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10441 entries, 0 to 10440
Data columns (total 65 columns):
id 10441 non-null int64
club 10441 non-null int64
league 10441 non-null int64
birth_date 10441 non-null object
height_cm 10441 non-null int64
weight_kg 10441 non-null int64
nationality 10441 non-null int64
potential 10441 non-null int64
...
dtypes: float64(12), int64(50), object(3)
memory usage: 5.2+ MB
None

2、DataFrame.query()

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('dataset/soccer/train.csv')
print(data.query('lw>cf')) # 这两个方法是等价的
print(data[data.lw > data.cf]) # 这两个方法是等价的

3、DataFrame.value_counts()

这个函数可以统计某一列中不同值出现的频率。

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('dataset/soccer/train.csv')
print(data.work_rate_att.value_counts())
Medium 7155
High 2762
Low 524
Name: work_rate_att, dtype: int64

4、DataFrame.sort_values()

按照某一列的数值进行排序后输出。

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('dataset/soccer/train.csv')
print(data.sort_values(['sho']).head(5))

5、DataFrame.groupby()

  • 根据国籍(nationality)这一列的属性进行分组,然后分别计算相同国籍的潜力(potential)的平均值。
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('dataset/soccer/train.csv')
potential_mean = data['potential'].groupby(data['nationality']).mean().head(5)
print(potential_mean)
nationality
1 74.945338
2 72.914286
3 67.892857
4 69.000000
5 70.024242
Name: potential, dtype: float64
  • 根据国籍(nationality),俱乐部(club)这两列的属性进行分组,然后分别计算球员潜力(potential)的平均值。
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('dataset/soccer/train.csv')
potential_mean = data['potential'].head(20).groupby([data['nationality'], data['club']]).mean()
print(potential_mean)
nationality club
1 148 76
461 72
5 83 64
29 593 68
43 213 67
51 258 62
52 112 68
54 604 81
63 415 70
64 359 74
78 293 73
90 221 70
96 80 72
101 458 67
111 365 64
379 83
584 65
138 9 72
155 543 72
163 188 71
Name: potential, dtype: int64

值得注意的是,在分组函数后面使用一个size()函数可以返回带有分组大小的结果。

potential_mean = data['potential'].head(200).groupby([data['nationality'], data['club']]).size()
nationality club
1 148 1
43 213 1
51 258 1
52 112 1
54 604 1
78 293 1
96 80 1
101 458 1
155 543 1
163 188 1
Name: potential, dtype: int64

6、DataFrame.agg()

这个函数一般在groupby函数之后使用。

import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('dataset/soccer/train.csv')
potential_mean = data['potential'].head(10).groupby(data['nationality']).agg(['max', 'min'])
print(potential_mean)
 max min
nationality
1 76 76
43 67 67
51 62 62
52 68 68
54 81 81
78 73 73
96 72 72
101 67 67
155 72 72
163 71 71

7、DataFrame.apply()

将某一个函数应用到某一列或者某一行上,可以极大加快处理速度。

import pandas as pd
import matplotlib.pyplot as plt
# 返回球员出生日期中的年份
def birth_date_deal(birth_date):
year = birth_date.split('/')[2]
return year
data = pd.read_csv('dataset/soccer/train.csv')
result = data['birth_date'].apply(birth_date_deal).head()
print(result)
0 96
1 84
2 99
3 88
4 80
Name: birth_date, dtype: object

当然如果使用lambda函数的话,代码会更加简洁:

data = pd.read_csv('dataset/soccer/train.csv')
result = data['birth_date'].apply(lambda x: x.split('/')[2]).head()
print(result)
版权声明
本文为[qinjianhuang]所创,转载请带上原文链接,感谢
https://huangqinjian.blog.csdn.net/article/details/79685891

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