pandas_dataframe01

1. 如何从csv文件只读取前几行的数据

   # 只读取前2行和指定列的数据
   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv',nrows=2,usecols=['Model','Length'])
   df
   
   #>        Model    Length
   0    Integra    177
   1    Legend    195

2. 如何从csv文件中每隔n行来创建dataframe

   # 每隔50行读取一行数据
   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', chunksize=50)
   df2 = pd.DataFrame()
   for chunk in df:
       # 获取series
       df2 = df2.append(chunk.iloc[0,:])
   
   #显示前5行
   print(df2.head())
   
   #>                      crim    zn  indus chas                  nox     rm   age  \
       0              0.21977   0.0   6.91    0  0.44799999999999995  5.602  62.0   
       1               0.0686   0.0   2.89    0                0.445  7.416  62.5   
       2   2.7339700000000002   0.0  19.58    0                0.871  5.597  94.9   
       3               0.0315  95.0   1.47    0  0.40299999999999997  6.975  15.3   
       4  0.19072999999999998  22.0   5.86    0                0.431  6.718  17.5   
       
             dis rad  tax ptratio       b  lstat  medv  
       0  6.0877   3  233    17.9   396.9   16.2  19.4  
       1  3.4952   2  276    18.0   396.9   6.19  33.2  
       2  1.5257   5  403    14.7  351.85  21.45  15.4  
       3  7.6534   3  402    17.0   396.9   4.56  34.9  
       4  7.8265   7  330    19.1  393.74   6.56  26.2  

3. 如何改变导入csv文件的列值
   改变列名‘medv’的值，当列值≤25时，赋值为‘Low’；列值>25时，赋值为‘High’.

   # 使用converters参数，改变medv列的值
   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', 
                    converters={'medv': lambda x: 'High' if float(x) > 25 else 'Low'})
   print(df.head())
   
   #>            b  lstat  medv
       0  396.90   4.98   Low  
       1  396.90   9.14   Low  
       2  392.83   4.03  High  
       3  394.63   2.94  High  
       4  396.90   5.33  High 

4. 如何从csv文件导入指定的列

   # 导入指定的列：crim和medv
   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv', usecols=['crim', 'medv'])
   # 打印前四行dataframe信息
   print(df.head())
   
   #>          crim  medv
       0  0.00632  24.0
       1  0.02731  21.6
       2  0.02729  34.7
       3  0.03237  33.4
       4  0.06905  36.2

5. 如何得到dataframe的行，列，每一列的类型和相应的描述统计信息

   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
   
   #  打印dataframe的行和列
   print(df.shape)
   
   # 打印dataframe每列元素的类型显示前5行
   print(df.dtypes.head())
   
   # 统计各类型的数目,方法1
   print(df.get_dtype_counts())
   # 统计各类型的数目,方法2
   # print(df.dtypes.value_counts())
   
   # 描述每列的统计信息，如std，四分位数等
   df_stats = df.describe()
   # dataframe转化数组
   df_arr = df.values
   # 数组转化为列表
   df_list = df.values.tolist()
   
   #>    (93, 27)
       Manufacturer     object
       Model            object
       Type             object
       Min.Price       float64
       Price           float64
       dtype: object
       float64    18
       object      9
       dtype: int64

6. 如何获取给定条件的行和列

   import numpy as np
   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
   # print(df)
   # 获取最大值的行和列
   row, col = np.where(df.values == np.max(df.Price))
   # 行和列获取最大值
   print(df.iat[row[0], col[0]])
   df.iloc[row[0], col[0]]
   
   # 行索引和列名获取最大值
   df.at[row[0], 'Price']
   df.get_value(row[0], 'Price')
   
   #>    61.9