合并两个 DataFrame 后,可以迭代合并结果进行进一步分析。 importpandasaspd data1={'ID':[1,2,3],'Name':['Alice','Bob','Charlie']}data2={'ID':[1,2,3],'Salary':[50000,60000,70000]}df1=pd.DataFrame(data1)
'Amit','Aishwarya','Priyanka'],'Age':[21,19,20,18],'Stream':['Math','Commerce','Arts','Biology'],'Percentage':[88,92,95,70]}# Convert the dictionary into DataFramedf=pd.DataFrame(data,columns=['Name','Age','Stream','Percentage'])print("Given Dataframe...
我们还可以将DataFrame转换为一个数组,遍历该数组以对每行(存储在列表中)执行操作,然后将该列表转换回DataFrame。 start = time.time() # create an empty dictionary list2 = [] # intialize column having 0s. df['e'] = 0 # iterate through a NumPy array for row in df.values: if row[0] == ...
我们还可以将DataFrame转换为一个数组,遍历该数组以对每行(存储在列表中)执行操作,然后将该列表转换回DataFrame。 start = time.time() # create an empty dictionary list2 = [] # intialize column having 0s. df['e'] = 0 # iterate through a NumPy array for row ...
# iterate through each row and select # 'Name' and 'Stream' column respectively. for ind in df.index: print(df['Name'][ind], df['Stream'][ind]) 输出 Given Dataframe : Name Age Stream Percentage 0 Ankit 21 Math 88 1 Amit 19 Commerce 92 ...
我们还可以将DataFrame转换为一个数组,遍历该数组以对每行(存储在列表中)执行操作,然后将该列表转换回DataFrame。 start = time.time() # create an empty dictionary list2 = [] # intialize column having 0s. df['e'] = 0 # iterate through a NumPy array for row in df.values: if row[0] ==...
f= codecs.open(filePath,'w','utf-8') f.write(cont) f.flush() f.close() 参考链接:http://stackoverflow.com/questions/15125343/how-to-iterate-through-two-pandas-columns 生活不易,本人有意向做数据分析兼职或python在线辅导,如有需要请联系qq号1334832194。
How to iterate over rows in a DataFrame in Pandas-DataFrame按行迭代 https://stackoverflow.com/questions/16476924/how-to-iterate-over-rows-in-a-dataframe-in-pandas http://stackoverflow.com/questions/7837722/what-is-the-most-efficient-way-to-loop-through-dataframes-with-pandas ...
我们还可以将DataFrame转换为一个数组,遍历该数组以对每行(存储在列表中)执行操作,然后将该列表转换回DataFrame。 start = time.time() # create an empty dictionary list2 = [] # intialize column having 0s. df['e'] = 0 # iterate through a NumPy array ...
(t2) #%% Create the body of the new table table3 = pd.DataFrame(np.nan, columns=['letter','number2'], index=[0]) #%% Iterate through filtering relevant data, optimizing, returning info for row_index, row in table1.iterrows(): t2info = table2[table2.letter == row['letter']]....