1、將下面字典建立為DataFrame

data = {"grammer":["Python","C","Java","GO",np.nan,"SQL","PHP","Python"],
 "score":[1,2,np.nan,4,5,6,7,10]}
df = pd.DataFrame(data)

2、提取含有字串Python的行

df[df['grammer'] == 'Python']

3、輸出df的所有列名

df.columns

4、修改第二列列名為'popularity'

df.rename(columns={'score':'popularity'},inplace = True)
# inplace:是否原地替換。布林值，預設為False。如果為True，則在原DataFrame上進行操作，返回值為None。

5、統計grammer列中每種程式語言出現的次數

df['grammer'].value_counts()

6、將空值用上下值的平均值填充

df['popularity'] = df['popularity'].fillna(df['popularity'].interpolate())
#fillna(method引數控制向上還是向下填充，ffill：向下自動填充，bfill：向上自動填充)
 

7、提取popularity列中值大於3的行

df[df['popularity'] > 3]

8、按照grammer列進行去除重複值

df.drop_duplicates(['grammer'])

9、計算popularity列平均值

df['popularity'].mean()

10、將grammer列轉換為list

df['grammer'].to_list()

11、將DataFrame儲存為EXCEL

df.to_excel('test.xlsx')

12、檢視資料行列數

df.shape

13、提取popularity列值大於3小於7的行

df[(df['popularity'] > 3) & (df['popularity'] < 7)]
 

14、交換兩列位置

temp = df['popularity']
df.drop(labels=['popularity'],axis=1,inplace=True)
df.insert(0,'popularity',temp)

15、提取popularity列最大值所在行

df[df['popularity'] == df['popularity'].max()]