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Python 使用带过滤器的pandas写入csv_Python_Csv_Pandas_Dataframe - Fatal编程技术网

Python 使用带过滤器的pandas写入csv

Python 使用带过滤器的pandas写入csv,python,csv,pandas,dataframe,Python,Csv,Pandas,Dataframe,我正在使用pandas库使用Python加载csv文件 import pandas as pd df = pd.read_csv("movies.csv") 然后,我检查列中的特定值或语句,例如: viewNum = df["views"] >= 1000 starringActorNum = df["starring"] > 3 df["title"] = df["title"].astype("str") titleLen = df["title"].str.len() <

我正在使用pandas库使用Python加载csv文件

import pandas as pd
df = pd.read_csv("movies.csv")
然后,我检查列中的特定值或语句,例如:

viewNum = df["views"] >= 1000
starringActorNum = df["starring"] > 3
df["title"] = df["title"].astype("str")
titleLen = df["title"].str.len() <= 10
viewNum=df[“视图”]>=1000
starringActorNum=df[“starring”]>3
df[“title”]=df[“title”].astype(“str”)

titleLen=df[“title”].str.len()使用
组合布尔掩码(按位和):

选择
df
的行,其中
mask
为真:

df_filtered = df.loc[mask]
将数据帧写入csv:

df_filtered.to_csv('movies-filtered.csv')

将熊猫作为pd导入
df=pd.read\u csv(“movies.csv”)
viewNum=df[“视图”]>=1000
starringActorNum=df[“starring”]>3
df[“title”]=df[“title”].astype(“str”)
titleLen=df[“title”].str.len()您可以使用该接口。它允许文本字符串查询,并且对于大型数据集非常快速

像这样的方法应该会奏效:

import pandas as pd
df = pd.read_csv("movies.csv")

# the len() method is not available to query, so pre-calculate
title_len = df["title"].str.len()

# build the data frame and send to csv file, title_len is a local variable
df.query('views >= 1000 and starring > 3 and @title_len <= 10').to_csv(...)
将熊猫作为pd导入
df=pd.read\u csv(“movies.csv”)
#len()方法不可用于查询,因此请预先计算
title_len=df[“title”]。str.len()
#构建数据帧并发送到csv文件,title_len是一个局部变量

df.query('views>=1000,starring>3和@title\len)您想让您的数据框过滤,并同时应用所有的约束条件吗?是的。我已经加载了一个.csv数据集,但希望根据某些条件过滤掉一些内容(因此,上面的示例).我只是不知道如何将我所有的约束转换为csv
import pandas as pd

df = pd.read_csv("movies.csv")

viewNum = df["views"] >= 1000
starringActorNum = df["starring"] > 3
df["title"] = df["title"].astype("str")
titleLen = df["title"].str.len() <= 10

mask = viewNum & starringActorNum & titleLen
df_filtered = df.loc[mask]

df_filtered.to_csv('movies-filtered.csv')
import pandas as pd
df = pd.read_csv("movies.csv")

# the len() method is not available to query, so pre-calculate
title_len = df["title"].str.len()

# build the data frame and send to csv file, title_len is a local variable
df.query('views >= 1000 and starring > 3 and @title_len <= 10').to_csv(...)