从 .txt 文件读取时，Python Pandas 无法识别数字

Question

I'm using Pandas to write to table from .txt file generated by other C++ program. Python or Pandas do not recognize them as numbers and I'm really clueless what to do. Here is Python code:

df = pd.read_csv(r'C:\Users\romea\Desktop\Inżynierka\ER_etap_5\Metropolis_average_path_6_p_0.010000_nodes_100.txt', sep='\t', engine='python', header=None)
df.columns = df.iloc[0]
df = df.drop([0])
df.head()

[![Head of data frame][1]][1]

And here it is when i try plot it:

plt.scatter(df["iteracja"], df["variancja"])
plt.show()

Check out that values on y-axis are nonsens. Here is the .txt file. Separation with "\\t" and new line with std::endl from C++:

iteracja    krawedzie   srednia_chwil   variancja
0   10000   10000   0.138686
100 2843.07 2843.07 0.991797
200 16.0296 16.0296 0.263918
300 4.55257 4.55257 0.235237
400 4.5834  4.5834  0.217816
500 4.68072 4.68072 0.167809
600 4.78377 4.78377 0.129301
700 4.83168 4.83168 0.109151
800 4.8534  4.8534  0.0963009
900 5.66296 5.66296 0.0710574
1000    5.96965 5.96965 0.0485687

And here is the part of C++ code that generates it:

myfile<<"iteracja"<<"\t"<<"krawedzie"<<"\t"<<"srednia_chwil"<<"\t"<<"variancja"<<endl;
        std::vector <float> temp_curent;
        std::vector <float> temp_edges;
        for(int i = 0; i<break_counter; i++)
        {
            temp_curent.push_back(current_average_array[i]);
            temp_edges.push_back(edge_array[i]);
            if (i % 100 == 0)
            {
                double suma = 0;
                double suma_edges = 0;
                for(int j = 0; j<temp_curent.size(); j++)
                {
                    suma_edges += temp_curent[j];
                    suma += temp_curent[j];
                }
                suma /= 100;
                suma_edges /= 100;
                cout<<i<<"\t"<<suma_edges<<"\t"<<suma<<"\t"<<variance_fractorial[(int)((i/100))]<<endl;
                myfile<<i<<"\t"<<suma_edges<<"\t"<<suma<<"\t"<<variance_fractorial[(int)((i/100))]<<endl;
                temp_curent.clear();
                temp_edges.clear();
            }
        }
        myfile.close();
        return break_counter;

Answer 1

import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv(r'your_file_here.txt', sep='\t', header=0)
plt.scatter(df["iteracja"], df["variancja"])
plt.show()