(2, 3.0, "World")] In [50]: pd.DataFrame(data) Out[50]: A B C 0 1 2.0 b'Hello' 1 2 3.0 b'World' In [51]: pd.DataFrame(data, index=["first", "second"]) Out[51]: A B C first 1 2.0 b'Hello' second
In [7]: df.info(memory_usage="deep") <class 'pandas.core.frame.DataFrame'> RangeIndex: 5000 entries, 0 to 4999 Data columns (total 8 columns): # Column Non-Null Count Dtype --- --- --- --- 0 int64 5000 non-null int64 1 float64 5000 non-null float64 2 datetime64[ns] 5000...
将date变量,转化为 pandas 中的 datetine 变量 df.info()<class'pandas.core.frame.DataFrame'>RangeIndex:360entries,0to359Datacolumns(total5columns):# Column Non-Null Count Dtype---0id360non-nullint641date360non-nulldatetime64[ns]2产品360non-nullobject3销售额360non-nullfloat644折扣360non-nullfl...
df.info()"""<class'pandas.core.frame.DataFrame'>RangeIndex:1000000entries,0to999999Datacolumns(total14columns): #ColumnNon-NullCountDtype---0CID1000000non-nullobject1Name1000000non-nullobject2Age1000000non-nullint643City1000000non-nullobject4Plate1000000non-nullobject5Job1000000non...
# count of each unique value in the "Gender" column print(df['Gender'].value_counts()) Output: Male 3 Female 2 Name: Gender, dtype: int64 In the above example, the pandas seriesvalue_counts()function is used to get the counts of'Male'and'Female', the distinct values in the column...
# Column Non-Null Count Dtype--- --- --- ---0 Python 100 non-null int32 #Python列有100个非空值,数据类型为int321 Math 100 non-null int32 #Math列有100个非空值,数据类型为int322 En 100 non-null int32 #En列有100个非空值,数据类型为int32dtypes: int32(3)memory usage: 1.3 KB ...
Counting occurrences of False or True in a column in pandasWe will count these bool values with the help of the value_count() method. The value_count() which will return the count of total occurrences of each value and it encapsulates all the similar values into 1 single entity and ...
In [19]: s2 = pd.Series(["a", None, "b"], dtype="object") In [20]: s2.str.count("a") Out[20]: 0 1.0 1 NaN 2 0.0 dtype: float64 In [21]: s2.dropna().str.count("a") Out[21]: 0 1 2 0 dtype: int64 当存在 NA 值时,输出 dtype 为 float64。对于返回布尔值的方法...
# Column Non-Null Count Dtype --- --- --- --- 0 Datetime 145366 non-null object 1 PJME_MW 145366 non-null float64 dtypes: float64(1), object(1) memory usage: 2.2+ MB None RangeIndex: 143206 entries, 0 to 143205 Data columns (total 2 columns...
>>> df.groupby('colB')['colB'].count()5.0 2 6.0 1 15.0 3 Name: colB, dtype: int64 Note that the above expression will give the frequencies for every non-null value appearing in the column specified. Using value_counts Alternatively, we can use thepandas.Series.value_counts()method whi...