AI代码解释 df=pd.DataFrame({'year':[2015,2016],'month':[2,3],'day':[4,5]})df['month']=df['month'].map(str)df.info()>><class'pandas.core.frame.DataFrame'>RangeIndex:2entries,0to1Datacolumns(total3columns):# Column Non-Null Count Dtype---0year2non-nullint641month2non-nullob...
# Column Non-Null Count Dtype --- --- --- --- 0 string_col 4 non-null object 1 int_col 4 non-null int64 2 float_col 4 non-null float64 3 mix_col 4 non-null object 4 missing_col 3 non-null float64 5 money_col 4 non-null object 6 boolean_col 4 non-null bool 7 custom...
In [58]: mask = pd.array([True, False, True, False, pd.NA, False], dtype="boolean") In [59]: mask Out[59]: <BooleanArray> [True, False, True, False, <NA>, False] Length: 6, dtype: boolean In [60]: df1[mask] Out[60]: A B C D a 0.132003 -0.827317 -0.076467 -1.1876...
Example 1: Convert Boolean Data Type to String in Column of pandas DataFrame In Example 1, I’ll demonstrate how to transform a True/False logical indicator to the string data type. For this task, we can use the map function as shown below: ...
(key) 1123 # Convert generator to list before going through hashable part 1124 # (We will iterate through the generator there to check for slices) 1125 if is_iterator(key): File ~/work/pandas/pandas/pandas/core/series.py:1237, in Series._get_value(self, label, takeable) 1234 return ...
RangeIndex:6entries,0to5 Datacolumns(total4columns): #ColumnNon-NullCountDtype --- 0a6non-nullint64 1b6non-nullbool 2c6non-nullfloat64 3d6non-nullobject dtypes:bool(1),float64(1),int64(1),object(1) memoryusage:278.0+bytes 1. 2. 3. 4...
批量元素改变,Series专有 df['Keras'].map({1:'Hello',5:'World',7:'AI'}) # 字典映射 df['Python'].map(lambda x:True if x >=5 else False) # 隐式函数映射 def convert(x): # 显示函数映射 if x%3 == 0: return True elif x%3 == 1: return False df['Tensorflow'].map(convert...
index_col : string or list of strings, optional, default: None Column(s) to set as index(MultiIndex). coerce_float : boolean, default True Attempts to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets. params : list, ...
dtype=None, #boolean或dict,默认为Trueconvert_axes=None, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False, # 布尔值,默认为False,每行读取该文件作为json对象 chunksize=None, ...
#intdf["Age"].memory_usage(index=False, deep=False)#8000000#convertdf["Age"] = df["Age"].astype('int8')df["Age"].memory_usage(index=False, deep=False)#1000000#floatdf["Salary_After_Tax"] = df["Salary"] * 0.6df["Salary_After_Tax"].memory_usage(index=False, deep=False)#...