pandas dataframe 过滤——apply最灵活!!!
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按照某特定string字段长度过滤:
import pandas as pd df = pd.read_csv(‘filex.csv‘) df[‘A‘] = df[‘A‘].astype(‘str‘) df[‘B‘] = df[‘B‘].astype(‘str‘) mask = (df[‘A‘].str.len() == 10) & (df[‘B‘].str.len() == 10) df = df.loc[mask] print(df)
Applied to filex.csv:
A,B
123,abc
1234,abcd
1234567890,abcdefghij
the code above prints
A B
2 1234567890 abcdefghij
或者是:
data={"names":["Alice","Zac","Anna","O"],"cars":["Civic","BMW","Mitsubishi","Benz"], "age":["1","4","2","0"]} df=pd.DataFrame(data) """ df: age cars names 0 1 Civic Alice 1 4 BMW Zac 2 2 Mitsubishi Anna 3 0 Benz O Then: """ df[ df[‘names‘].apply(lambda x: len(x)>1) & df[‘cars‘].apply(lambda x: "i" in x) & df[‘age‘].apply(lambda x: int(x)<2) ] """ We will have : age cars names 0 1 Civic Alice """
最灵活的是用apply:
def load_metadata(dir_name): columns_index_list = [ MetaIndex.M_METADATA_ID_INDEX, MetaIndex.M_SRC_IP_INDEX, MetaIndex.M_DST_IP_INDEX, MetaIndex.M_SRC_PORT_INDEX, MetaIndex.M_DST_PORT_INDEX, MetaIndex.M_PROTOCOL_INDEX, MetaIndex.M_HEADER_H, MetaIndex.M_PAYLOAD_H, MetaIndex.M_TCP_FLAG_H, MetaIndex.M_FLOW_FIRST_PKT_TIME, MetaIndex.M_FLOW_LAST_PKT_TIME, MetaIndex.M_OCTET_DELTA_COUNT_FROM_TOTAL_LEN, ] columns_name_list = [ "M_METADATA_ID_INDEX", "M_SRC_IP_INDEX", "M_DST_IP_INDEX", "M_SRC_PORT_INDEX", "M_DST_PORT_INDEX", "M_PROTOCOL_INDEX", "M_HEADER_H", "M_PAYLOAD_H", "M_TCP_FLAG_H", "M_FLOW_FIRST_PKT_TIME", "M_FLOW_LAST_PKT_TIME", "M_OCTET_DELTA_COUNT_FROM_TOTAL_LEN", ] def metadata_parse_filter(row): try: if row[‘M_PROTOCOL_INDEX‘] != 6: return False if len(row[‘M_HEADER_H‘]) < 2 or len(row[‘M_PAYLOAD_H‘]) < 2 or not is_l34_tcp_metadata(row[‘M_METADATA_ID_INDEX‘]): return False first_time = row[‘M_FLOW_FIRST_PKT_TIME‘].split(‘-‘) last_time = row[‘M_FLOW_LAST_PKT_TIME‘].split(‘-‘) flow_first_pkt_time = int(first_time[0]) rev_flow_first_pkt_time = int(first_time[1]) flow_last_pkt_time = int(last_time[0]) rev_flow_last_pkt_time = int(last_time[1]) if flow_first_pkt_time > flow_last_pkt_time or rev_flow_first_pkt_time > rev_flow_last_pkt_time: return False return True except Exception as e: return False for root, dirs, files in os.walk(dir_name): for filename in files: file_path = os.path.join(root, filename) df = pd.read_csv(file_path, delimiter=‘^‘, usecols=columns_index_list, names=columns_name_list, encoding=‘utf-8‘, error_bad_lines=False, warn_bad_lines=True, header=0, lineterminator=" ") filter_df = df.loc[df.apply(metadata_parse_filter, axis=1)] yield filter_df
直接按照row过滤!
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