In [8]: columns = ["id_0", "name_0", "x_0", "y_0"] In [9]: pd.read_parquet("timeseries_wide.parquet")[columns] Out[9]: id_0 name_0 x_0 y_0 timestamp 2000-01-01 00:00:00 977 Alice -0.821225 0.906222 2000-01-01 00:01:00 1018 Bob -0.219182 0.350855 2000-01-01 ...
In[5]:pd.to_datetime(s,infer_datetime_format=True)Out[5]:02000-03-1112000-03-1222000-03-13dtype:datetime64[ns]# 还可以将时间戳转化为日期 In[6]:s=pd.Series([1490195805,1590195805,1690195805])In[7]:pd.to_datetime(s,unit='s')Out[7]:02017-03-2215:16:4512020-05-2301:03:2522023-07...
# Drop all the rows where at least one element is missingdf = df.dropna() # or df.dropna(axis=0) **(axis=0 for rows and axis=1 for columns)# Note: inplace=True modifies the DataFrame rather than creating a new onedf.dropna(inplace=True)# Drop all the columns where at least...
pandas中常见的数据类型 1.to_numeric()/to_datetime #pd.to_datetime#pd.to_datetime用于处理成组日期,不管这些日期是DataFrame的轴索引还是列,to_datetime方法可以解析多种不同的日期表示形式#例如:df['date_formatted']=pd.to_datetime(df['date'],format='%Y-%m-%d')#是可以通过apply()方法进行多列的操作...
pandas.to_numeric(arg,errors='raise',downcast=None)arg：表示要转换的数据，可以是list、tuple、...
df.columns = df.columns.str.replace(' ','_') 上述三个函数的结果都一样,可以更改列名使得列名中不含有空格: df 最后,如果你需要在列名中添加前缀或者后缀,你可以使用add_prefix()函数: df.add_prefix('X_') 或者使用add_suffix()函数: df.add_suffix...
df.dropna() # 一行中有一个缺失值就删除df.dropna(axis='columns') # 只保留全有值的列df.dropna(how='all') # 行或列全没值才删除df.dropna(thresh=2) # 至少有两个空值时才删除df.dropna(inplace=True) # 删除并使替换生效 05、高级过滤 ...
apply(pd.to_numeric, errors='coerce').fillna(0) df Trick 8 缩减数据的体积 drinks.info(memory_usage='deep') ## 30.5 KB <class 'pandas.core.frame.DataFrame'> RangeIndex: 193 entries, 0 to 192 Data columns (total 6 columns): # Column Non-Null Count Dtype --- --- --- --- 0 ...
(f, axis="columns") File ~/work/pandas/pandas/pandas/core/frame.py:10374, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs) 10360 from pandas.core.apply import frame_apply 10362 op = frame_apply( 10363 self, 10364 func=func, ...
df['year']=pd.to_numeric(df['year'].str.replace('年建',''),errors="coerce").astype('Int64')df.info()<class'pandas.core.frame.DataFrame'>RangeIndex:31568entries,0to31567Data columns(total4columns):# Column Non-Null Count Dtype---0floor31219non-nullobject1year12850non-null Int642area3...