Pandas在处理大数据(尤其是列比较多的场景)时，如果不做优化，内存占用还是很大的，下面通过一个实例来说明可以怎样优化
首先，生成一批18万的数据，每条数据151列
import pandas as pd import numpy as np def gen_big_data(csv_file: str, big_data_count=90000): chars = 'abcdefghijklmnopqrstuvwxyz' dates = pd.date_range(start='2020-01-01', periods=big_data_count, freq='30s') big_data_cols = ['Name'] for group in range(1, 31): big_data_cols.extend([f'date str {group}', f'bool {group}', f'int {group}', f'float {group}', f'str {group}']) big_data = [] for i in range(0, big_data_count): row = [f'Name Item {(i + 1)}'] for _ in range(0, 30): row.extend([str(dates[i]), i % 2 == 0, np.random.randint(10000, 100000), 10000 * np.random.random(), chars[np.random.randint(0, 26)] * 15]) big_data.append(row) df = pd.DataFrame(data=big_data, columns=big_data_cols) df.to_csv(csv_file, index=None) if __name__ == '__main__': # 修改存放路径以及模拟数据量(默认9万) gen_big_data('./files/custom_big_data.csv', 180000)查看生成的数据格式，可以看到每一行有151列
保存数据后，先查看一下内存占用情况
import pandas as pd def info_memory(csv_file: str): df = pd.read_csv(csv_file) print(df.info(memory_usage='deep')) if __name__ == '__main__': info_memory('./files/custom_big_data.csv')打印结果如下，可以看到当前内存占用为862.1MB
<class 'pandas.core.frame.DataFrame'> RangeIndex: 180000 entries, 0 to 179999 Columns: 151 entries, Name to str 30 dtypes: bool(30), float64(30), int64(30), object(61) memory usage: 862.1 MB查看不同类型的内存占用情况
def info_memory_by_d_type(csv_file: str): df = pd.read_csv(csv_file) for d_type in ['bool', 'float64', 'int64', 'object']: d_type_selected = df.select_dtypes(include=[d_type]) mem_mean_bit = d_type_selected.memory_usage(deep=True).mean() mem_mean_mb = mem_mean_bit / 1024 ** 2 print(f'mean memory usage: {d_type:<7} - {mem_mean_mb:.3f} M')输出结果如下，其中object类型占用内存最多
mean memory usage: bool - 0.166 M mean memory usage: float64 - 1.329 M mean memory usage: int64 - 1.329 M mean memory usage: object - 12.494 M查看某个类型的内存占用量
def info_mem_usage_mb(pd_obj): if isinstance(pd_obj, pd.DataFrame): mem_usage = pd_obj.memory_usage(deep=True).sum() else: mem_usage = pd_obj.memory_usage(deep=True) # 转换为MB返回 return f'{mem_usage / 1024 ** 2:02.3f} MB'int和float类型
对于int和float类型的数据，Pandas加载到内存中的数据，默认是int64和float64。一般场景下的数据，用int32和float32就足够了，用numpy.iinfo和numpy.finfo可以打印对应类型的取值范围
Machine parameters for int32 --------------------------------------------------------------- min = -2147483648 max = 2147483647 --------------------------------------------------------------- Machine parameters for int64 --------------------------------------------------------------- min = -9223372036854775808 max = 9223372036854775807 ---------------------------------------------------------------Machine parameters for float32 --------------------------------------------------------------- ... maxexp = 128 max = 3.4028235e+38 nexp = 8 min = -max --------------------------------------------------------------- Machine parameters for float64 --------------------------------------------------------------- ... maxexp = 1024 max = 1.7976931348623157e+308 nexp = 11 min = -max ---------------------------------------------------------------分别优化int和float的类型
def optimize_int_and_float(): df_int = df.select_dtypes(include=['int64']) df_int_converted = df_int.apply(pd.to_numeric, downcast='unsigned') df_float = df.select_dtypes(include=['float64']) df_float_converted = df_float.apply(pd.to_numeric, downcast='float') print('int before ', info_mem_usage_mb(df_int)) print('int converted ', info_mem_usage_mb(df_int_converted)) print('float before ', info_mem_usage_mb(df_float)) print('float converted', info_mem_usage_mb(df_float_converted))优化后的结果如下，内存减少50%左右
int before 41.199 MB int converted 20.599 MB float before 41.199 MB float converted 20.599 MBobject类型中的普通str数据
获取object类型数据，并调用describe()展示统计信息
对于区分度较低的 str 1到str 30，一共只有26个可能的值，可以考虑转换为Pandas中的categroy类型，这里将区分度小于40%的列转换为category类型
def optimize_obj(): df_obj = df.select_dtypes(include=['object']) df_obj_converted = pd.DataFrame() for col in df_obj.columns: unique_count = len(df_obj[col].unique()) total_count = len(df_obj[col]) # 将区分度小于40%的列转换为category类型 if unique_count / total_count <= 0.4: df_obj_converted.loc[:, col] = df_obj[col].astype('category') else: df_obj_converted.loc[:, col] = df_obj[col] print('object before ', info_mem_usage_mb(df_obj)) print('object converted', info_mem_usage_mb(self.df_obj_converted))执行结果如下，降低了300+M的内存
object before 774.602 MB object converted 409.047 MBobject类型中的date数据
def optimize_date_str(): df_date = pd.DataFrame() df_date_converted = pd.DataFrame() for col_name in df.columns: if col_name.startswith('date str'): df_date.loc[:, col_name] = df[col_name] df_date_converted.loc[:, col_name] = pd.to_datetime(df[col_name]) print('date before ', info_mem_usage_mb(df_date)) print('date converted', info_mem_usage_mb(df_date_converted))执行结果如下，也降低了300+M的内存
date before 391.388 MB date converted 41.199 MB综合以上的优化方法，并封装为类PandasMemoryOptimizeDemo
import pandas as pd import numpy as np class PandasMemoryOptimizeDemo: df: pd.DataFrame df_int_converted: pd.DataFrame df_float_converted: pd.DataFrame df_obj_converted: pd.DataFrame df_date_converted: pd.DataFrame def __init__(self, csv_file: str): self.csv_file = csv_file self.df = pd.read_csv(self.csv_file) @staticmethod def info_mem_usage_mb(pd_obj): if isinstance(pd_obj, pd.DataFrame): mem_usage = pd_obj.memory_usage(deep=True).sum() else: mem_usage = pd_obj.memory_usage(deep=True) # 转换为MB返回 return f'{mem_usage / 1024 ** 2:02.3f} MB' def optimize_int_and_float(self): df_int = self.df.select_dtypes(include=['int64']) self.df_int_converted = df_int.apply(pd.to_numeric, downcast='unsigned') df_float = self.df.select_dtypes(include=['float64']) self.df_float_converted = df_float.apply(pd.to_numeric, downcast='float') print('int before ', self.info_mem_usage_mb(df_int)) print('int converted ', self.info_mem_usage_mb(self.df_int_converted)) print('float before ', self.info_mem_usage_mb(df_float)) print('float converted', self.info_mem_usage_mb(self.df_float_converted)) def optimize_obj(self): df_obj = self.df.select_dtypes(include=['object']) self.df_obj_converted = pd.DataFrame() for col in df_obj.columns: unique_count = len(df_obj[col].unique()) total_count = len(df_obj[col]) # 将区分度小于40%的列转换为category类型 if unique_count / total_count <= 0.4: self.df_obj_converted.loc[:, col] = df_obj[col].astype('category') else: self.df_obj_converted.loc[:, col] = df_obj[col] print('object before ', self.info_mem_usage_mb(df_obj)) print('object converted', self.info_mem_usage_mb(self.df_obj_converted)) def optimize_date_str(self): df_date = pd.DataFrame() self.df_date_converted = pd.DataFrame() for col_name in self.df.columns: if col_name.startswith('date str'): df_date.loc[:, col_name] = self.df[col_name] self.df_date_converted.loc[:, col_name] = pd.to_datetime(self.df[col_name]) print('date before ', self.info_mem_usage_mb(df_date)) print('date converted', self.info_mem_usage_mb(self.df_date_converted)) def optimize_all(self): self.optimize_int_and_float() self.optimize_obj() self.optimize_date_str() df_converted = self.df.copy() df_converted[self.df_int_converted.columns] = self.df_int_converted df_converted[self.df_float_converted.columns] = self.df_float_converted df_converted[self.df_obj_converted.columns] = self.df_obj_converted df_converted[self.df_date_converted.columns] = self.df_date_converted print('before ', self.info_mem_usage_mb(self.df)) print('converted', self.info_mem_usage_mb(df_converted)) if __name__ == '__main__': optimize_demo = PandasMemoryOptimizeDemo('./files/custom_big_data.csv') optimize_demo.optimize_all()执行结果如下，优化效果还是很明显的
before 862.149 MB converted 105.207 MB写代码的过程中，如果每次都按照这样的步骤，其实还是很繁琐，那能不能在调用read_csv方法时就进行优化呢？
接下来就一起来探索一下
在PyCharm中，点击read_csv进入源码，发现该方法提供了非常丰富的参数(50+)，这里只列举需要的参数
def read_csv( filepath_or_buffer: FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str], # General Parsing Configuration dtype: DtypeArg | None = None, converters=None, # Datetime Handling parse_dates=None, infer_datetime_format=False, keep_date_col=False, date_parser=None, low_memory=_c_parser_defaults["low_memory"], memory_map=False, storage_options: StorageOptions = None, ): # locals() should never be modified kwds = locals().copy()可以直接指定dtype和parse_dates，最终代码如下
def big_data_optimized_read_csv(self): d_type_dict = {} date_indexes = [] for i in range(1, 31): d_type_dict[f'int {i}'] = 'int32' d_type_dict[f'float {i}'] = 'float32' d_type_dict[f'str {i}'] = 'category' date_indexes.append(5 * (i - 1) + 1) self.df = pd.read_csv(self.csv_file, dtype=d_type_dict, parse_dates=date_indexes) print('optimized read_csv: ', self.info_mem_usage_mb(self.df))执行结果如下，内存占用也大大降低了
optimized read_csv: 105.207 MB