Python 计算csv中范围内的数据

我有一些数据需要分解成可管理的块。根据以下数据，我需要计算x在第11列出现的次数（第7列为1），以及x在第11列出现的次数。我需要把它们放到csv的第一行。在此之后，我需要计算相同的事情，但第11列为以下括号：

0

---

“>0但0.05但0.1但此解决方案使用

numpy.histogram

。请参见下文

import csv
import numpy


def count(infile='data.csv', outfile='new.csv'):
    total_x = 0
    col7one_x = 0
    total_zeros = 0
    col7one_zeros = 0
    all_array = []
    col7one_array = []
    with open(infile, 'r') as fobj:
        reader = csv.reader(fobj)
        for line in reader:
            if line[10] == 'x':
                total_x += 1
                if line[6] == '1':
                    col7one_x += 1
            elif line[10] == '0':
                # assumes zero is represented as "0" and not as say, "0.0"
                total_zeros += 1
                if line[6] == '1':
                    col7one_zeros += 1
            else:
                val = float(line[10])
                all_array.append(val)
                if line[6] == '1':
                    col7one_array.append(val)

    bins = numpy.arange(0, 1.05, 0.05)
    hist_all, edges = numpy.histogram(all_array, bins=bins)
    hist_col7one, edges = numpy.histogram(col7one_array, bins=bins)

    with open(outfile, 'w') as fobj:
        writer = csv.writer(fobj)
        writer.writerow([col7one_x, total_x])
        writer.writerow([col7one_zeros, total_zeros])
        for row in zip(hist_col7one, hist_all):
            writer.writerow(row)


if __name__ == '__main__':
    count()

---

以下是添加两个新字段的解决方案：

import csv
import numpy


def count(infile='data.csv', outfile='new.csv'):
    bins = numpy.arange(0, 1.05, 0.05)

    total_x = 0
    col7one_x = 0

    total_zeros = 0
    col7one_zeros = 0

    all_array = []
    col7one_array = []

    with open(infile, 'r') as fobj:
        reader = csv.reader(fobj)
        for line in reader:
            if line[10] == 'x':
                total_x += 1
                if line[6] == '1':
                    col7one_x += 1
            elif line[10] == '0':
                # assumes zero is represented as "0" and not as say, "0.0"
                total_zeros += 1
                if line[6] == '1':
                    col7one_zeros += 1
            else:
                val = float(line[10])
                all_array.append(val)
                if line[6] == '1':
                    col7one_array.append(val)

    all_array = numpy.array(all_array)
    hist_all, edges = numpy.histogram(all_array, bins=bins)
    hist_col7one, edges = numpy.histogram(col7one_array, bins=bins)
    bin_ranges = ['%s:%s' % (x, y) for x, y in zip(bins[:-1], bins[1:])]

    digitized = numpy.digitize(all_array, bins)
    bin_means = [all_array[digitized == i].mean() if hist_all[i - 1] else 'n/a' for i in range(1, len(bins))]


    with open(outfile, 'w') as fobj:
        writer = csv.writer(fobj)
        writer.writerow(['x', 'n/a', col7one_x, total_x])
        writer.writerow(['0', 0 if total_zeros else 'n/a', col7one_zeros, total_zeros])
        for row in zip(bin_ranges, bin_means, hist_col7one, hist_all):
            writer.writerow(row)


if __name__ == '__main__':
    count()

这可能会奏效：

import numpy as np
import pandas as pd


column_names = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 
              'col7', 'col8', 'col9', 'col10', 'col11'] #names to be used as column labels.  If no names are specified then columns can be refereed to by number eg. df[0], df[1] etc.

df = pd.read_csv('data.csv', header=None, names=column_names) #header= None means there are no column headings in the  csv file

df.ix[df.col11 == 'x', 'col11']=-0.08 #trick so that 'x' rows will be grouped into a category >-0.1 and <= -0.05.  This will allow all of col11 to be treated as a numbers

bins = np.arange(-0.1, 1.0, 0.05) #bins to put col11 values in.  >-0.1 and <=-0.05 will be our special 'x' rows, >-0.05 and <=0 will capture all the '0' values.
labels = np.array(['%s:%s' % (x, y) for x, y in zip(bins[:-1], bins[1:])]) #create labels for the bins
labels[0] = 'x' #change first bin label to 'x'
labels[1] = '0' #change second bin label to '0'

df['col11'] = df['col11'].astype(float) #convert col11 to numbers so we can do math on them


df['bin'] = pd.cut(df['col11'], bins=bins, labels=False) # make another column 'bins' and put in an integer representing what bin the number falls into.Later we'll map the integer to the bin label


df.set_index('bin', inplace=True, drop=False, append=False) #groupby is meant to run faster with an index

def count_ones(x):
    """aggregate function to count values that equal 1"""
    return np.sum(x==1)

dfg = df[['bin','col7','col11']].groupby('bin').agg({'col11': [np.mean], 'col7': [count_ones, len]}) # groupby the bin number and apply aggregate functions to specified column.
dfg.index = labels[dfg.index]# apply labels to bin numbers

dfg.ix['x',('col11', 'mean')]='N/A' #mean of 'x' rows is meaningless
print(dfg)
dfg.to_csv('new.csv')

---

到目前为止你得到了什么？你知道如何读取文件吗？“计算出特定范围内的平均值“：这是data1值的平均值还是data2值的平均值？i、 e.第7列等于1的值的平均值或该范围内所有值的平均值？啊，好的点，该范围内所有值的平均值。回答得好，有机会得到评论吗？非常感谢：）您好答案当然有效，但是输出在新行之间有空格。为问题添加了更多细节和悬赏。请对两个脚本进行评论，非常感谢a请对两个脚本进行评论，非常感谢AEAI意识到没有必要对另一个脚本进行评论，但是您对此脚本提供的任何评论都将不胜感激。嘿，很抱歉再次询问，此代码工作完美，但我确实可以使用这些评论来帮助我编辑它。Hi@user650654再次感谢此代码，我已授予它奖金，然而，我真的非常感谢对代码的一些评论，使我能够更有效地进行反向工程。非常感谢AEAHey rtrwalker这段代码真的很好。我想知道你是否可以进一步评论。到目前为止，我还没有使用pandas，并且一直在尽我最大的努力操作并添加到这段代码中。但是没有用。我一直在youtube上看视频，试图抓住它。我想真正让我头疼的是

垃圾箱和
zip
的使用。虽然我已经获得了赏金，但如果我能更好地理解它，我可能会认为这是公认的答案。我最纠结的一行是
dfg=df[['bin'，'col7'，'col11']].groupby（'bin'）.agg（{'col11'：[np.mean]，'col7'：[count_ones，len]}）
事实上，如果能做到这一点，我很乐意为你分配50分的奖金！显然，一个问题上的第二笔赏金必须是100分赏金的两倍。我用100分赏金打开了一个完整的问题，致力于理解你提供的熊猫代码。请看这里：
x,n/a,data1,data2
0:0.05,0.02469,data1,data2
0.05:0.1,0.5469,data1,data2
....
....

import csv
import numpy


def count(infile='data.csv', outfile='new.csv'):
    total_x = 0
    col7one_x = 0
    total_zeros = 0
    col7one_zeros = 0
    all_array = []
    col7one_array = []
    with open(infile, 'r') as fobj:
        reader = csv.reader(fobj)
        for line in reader:
            if line[10] == 'x':
                total_x += 1
                if line[6] == '1':
                    col7one_x += 1
            elif line[10] == '0':
                # assumes zero is represented as "0" and not as say, "0.0"
                total_zeros += 1
                if line[6] == '1':
                    col7one_zeros += 1
            else:
                val = float(line[10])
                all_array.append(val)
                if line[6] == '1':
                    col7one_array.append(val)

    bins = numpy.arange(0, 1.05, 0.05)
    hist_all, edges = numpy.histogram(all_array, bins=bins)
    hist_col7one, edges = numpy.histogram(col7one_array, bins=bins)

    with open(outfile, 'w') as fobj:
        writer = csv.writer(fobj)
        writer.writerow([col7one_x, total_x])
        writer.writerow([col7one_zeros, total_zeros])
        for row in zip(hist_col7one, hist_all):
            writer.writerow(row)


if __name__ == '__main__':
    count()

import csv
import numpy


def count(infile='data.csv', outfile='new.csv'):
    bins = numpy.arange(0, 1.05, 0.05)

    total_x = 0
    col7one_x = 0

    total_zeros = 0
    col7one_zeros = 0

    all_array = []
    col7one_array = []

    with open(infile, 'r') as fobj:
        reader = csv.reader(fobj)
        for line in reader:
            if line[10] == 'x':
                total_x += 1
                if line[6] == '1':
                    col7one_x += 1
            elif line[10] == '0':
                # assumes zero is represented as "0" and not as say, "0.0"
                total_zeros += 1
                if line[6] == '1':
                    col7one_zeros += 1
            else:
                val = float(line[10])
                all_array.append(val)
                if line[6] == '1':
                    col7one_array.append(val)

    all_array = numpy.array(all_array)
    hist_all, edges = numpy.histogram(all_array, bins=bins)
    hist_col7one, edges = numpy.histogram(col7one_array, bins=bins)
    bin_ranges = ['%s:%s' % (x, y) for x, y in zip(bins[:-1], bins[1:])]

    digitized = numpy.digitize(all_array, bins)
    bin_means = [all_array[digitized == i].mean() if hist_all[i - 1] else 'n/a' for i in range(1, len(bins))]


    with open(outfile, 'w') as fobj:
        writer = csv.writer(fobj)
        writer.writerow(['x', 'n/a', col7one_x, total_x])
        writer.writerow(['0', 0 if total_zeros else 'n/a', col7one_zeros, total_zeros])
        for row in zip(bin_ranges, bin_means, hist_col7one, hist_all):
            writer.writerow(row)


if __name__ == '__main__':
    count()

import numpy as np
import pandas as pd


column_names = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 
              'col7', 'col8', 'col9', 'col10', 'col11'] #names to be used as column labels.  If no names are specified then columns can be refereed to by number eg. df[0], df[1] etc.

df = pd.read_csv('data.csv', header=None, names=column_names) #header= None means there are no column headings in the  csv file

df.ix[df.col11 == 'x', 'col11']=-0.08 #trick so that 'x' rows will be grouped into a category >-0.1 and <= -0.05.  This will allow all of col11 to be treated as a numbers

bins = np.arange(-0.1, 1.0, 0.05) #bins to put col11 values in.  >-0.1 and <=-0.05 will be our special 'x' rows, >-0.05 and <=0 will capture all the '0' values.
labels = np.array(['%s:%s' % (x, y) for x, y in zip(bins[:-1], bins[1:])]) #create labels for the bins
labels[0] = 'x' #change first bin label to 'x'
labels[1] = '0' #change second bin label to '0'

df['col11'] = df['col11'].astype(float) #convert col11 to numbers so we can do math on them


df['bin'] = pd.cut(df['col11'], bins=bins, labels=False) # make another column 'bins' and put in an integer representing what bin the number falls into.Later we'll map the integer to the bin label


df.set_index('bin', inplace=True, drop=False, append=False) #groupby is meant to run faster with an index

def count_ones(x):
    """aggregate function to count values that equal 1"""
    return np.sum(x==1)

dfg = df[['bin','col7','col11']].groupby('bin').agg({'col11': [np.mean], 'col7': [count_ones, len]}) # groupby the bin number and apply aggregate functions to specified column.
dfg.index = labels[dfg.index]# apply labels to bin numbers

dfg.ix['x',('col11', 'mean')]='N/A' #mean of 'x' rows is meaningless
print(dfg)
dfg.to_csv('new.csv')

                col7           col11
          count_ones  len       mean
x                  1    7        N/A
0                  2   21          0
0.15:0.2           2    2        0.2
0.2:0.25           9   22  0.2478632
0.25:0.3           0   13  0.2840755
0.3:0.35           0    5  0.3333333
0.45:0.5           0    4        0.5