pandas

问题 I have a CSV file which have content as belows and I want to calculate the cosine similarity from one the remaining ID in the CSV file. I have load it into a dataframe of pandas as follows: old_df['Vector']=old_df.apply(lambda row: np.array(np.matrix(row.Vector)).ravel(), axis = 1) l=[] for a in old_df['Vector']: l.append(a) A=np.array(l) similarities = cosine_similarity(A) The output looks fine. However, i do not know how to find which the GUID (or ID)similar to other GUID (or ID), and I

问题 I'm reading a file with numerical values. data = pd.read_csv('data.dat', sep=' ', header=None) In the text file, each row end with a space, So pandas wait for a value that is not there and add a "nan" at the end of each row. For example: 2.343 4.234 is read as: [2.343, 4.234, nan] I can avoid it using , usecols = [0 1] but I would prefer a more general solution 回答1: You can use regular expressions in your sep argument. Instead of specifying the separator to be one space, you can ask it to use

问题 I am working on the code below: # Resample, interpolate and inspect ozone data here data = data.resample('D').interpolate() data.info() # Create the rolling window ***rolling = data.rolling(360)['Ozone'] # Insert the rolling quantiles to the monthly returns data['q10'] = rolling.quantile(.1) data['q50'] = rolling.quantile(.5) data['q90'] = rolling.quantile(.9) # Plot the data data.plot() plt.show() For the starred line (***), I was wondering, can I use the following instead? data['Ozone']

问题 I have large csv files with size more than 10 mb each and about 50+ such files. These inputs have more than 25 columns and more than 50K rows. All these have same headers and I am trying to merge them into one csv with headers to be mentioned only one time. Option: One Code: Working for small sized csv -- 25+ columns but size of the file in kbs. import pandas as pd import glob interesting_files = glob.glob("*.csv") df_list = [] for filename in sorted(interesting_files): df_list.append(pd.read

问题 I am really beginning at python, but I am hours in this line, can't go anywhere without fixing it. cadastro_2019_10= pd.read_csv("inf_cadastral_fi_20191015.csv",delimiter=";")[["CNPJ_FUNDO","DENOM_SOCIAL","CLASSE"]] UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc9 in position 49: invalid continuation byte cadastro_2019_10= pd.read_csv("inf_cadastral_fi_20191015.csv",delimiter=";")[["CNPJ_FUNDO","DENOM_SOCIAL","CLASSE"]] again: UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc9

问题 y = pd.Series(x, copy=True,dtype=float) z = pd.Series(x, copy=True) a = pd.Series(x) f = pd.Series.copy(x) All the above expressions give the same output of x value and even after updating the x value the change is not reflecting. So I need to know what is the use of copy as argument and the series.copy() and also how to copy x series to some other series such that any changes made in x is reflected back in the new series also. If any thing is wrong or not possible please forgive me... I'm a

问题 How can I create a new column in pandas that is the result of the difference of two other columns consisting of strings? I have one column titled "Good_Address" which has entries like "123 Fake Street Apt 101" and another column titled "Bad_Address" which has entries like "123 Fake Street". I want the output in column "Address_Difference" to be " Apt101". I've tried doing: import pandas as pd data = pd.read_csv("AddressFile.csv") data['Address Difference'] = data['GOOD_ADR1'].replace(data[

问题 Have to left align a description column in the pandas dataframe in python. Similar to left or right align a cell in excel sheet. is there any solution for this? Image attached for reference. !Dataset 回答1: Try this df.style.set_properties(subset=["col1", "col2"], **{'text-align': 'right'}) 回答2: I think you can just remove the leading spaces. df.Description = df.Description.apply(lambda row: row.lstrip(' ')) 来源： https://stackoverflow.com/questions/53460941/how-to-left-align-a-dataframe-column

问题 How can I create a new column in pandas that is the result of the difference of two other columns consisting of strings? I have one column titled "Good_Address" which has entries like "123 Fake Street Apt 101" and another column titled "Bad_Address" which has entries like "123 Fake Street". I want the output in column "Address_Difference" to be " Apt101". I've tried doing: import pandas as pd data = pd.read_csv("AddressFile.csv") data['Address Difference'] = data['GOOD_ADR1'].replace(data[

问题 Is there a canonical way to compute the element-wise mean of a list of DataFrames with identical columns and indices? The best way I can think of is from functools import reduce dfs = [df1, df2, df3, df4, df5] reduce(lambda x, y: x.add(y), dfs) / len(dfs) 回答1: Use concat with mean per index values: df1 = pd.DataFrame({ 'C':[7,8,9], 'D':[1,3,5], }) df2 = pd.DataFrame({ 'C':[4,2,3], 'D':[7,1,0], }) df3 = pd.DataFrame({ 'C':[9,4,2], 'D':[1,7,1], }) from functools import reduce dfs = [df1, df2,