isayev

grep -n "pattern" file.csv | awk -F  ":" '{print $1}'

isayev

cat input.txt | awk '{ print length, $0 }' | sort -n -s | cut -d" " -f2- > input-sorted.txt

# reverse order
cat input.txt | awk '{ print length, $0 }' | sort -n -r | cut -d" " -f2- > input-sorted-rev.txt

isayev

data[data.columns[::-1]]

isayev

def pca(X, npc):
    n_samples, n_features = X.shape
    Xmean = np.mean(X, axis=0)
    U, s, Vt = scipy.sparse.linalg.svds(X - Xmean, k=npc)
    order = np.argsort(-s) # sort s in descending order

    # svds returns U, s, Vt sorder in ascending order. We want descending
    s = s[order]
    W = Vt[order,:]

isayev

import csv
import os
import pprint
import datetime
#splits file returns the files splitted and the number of files(current_piece)

import csv
def splits(filehandler, delimiter=',', row_limit=100000, 
    output_name_template='output_%s.csv', output_path='.', keep_headers=True):
    """

isayev

import matplotlib.pyplot as plt
from scipy.sparse import coo_matrix

def plot_coo_matrix(m):
    if not isinstance(m, coo_matrix):
        m = coo_matrix(m)
    fig = plt.figure()
    ax = fig.add_subplot(111, axisbg='black')
    ax.plot(m.col, m.row, 's', color='white', ms=1)
    ax.set_xlim(0, m.shape[1])

isayev

find . -depth -name '*(*' | while IFS= read -r f ; do mv -i "$f" "$(dirname "$f")/$(basename "$f"|tr '(' _)" ; done
find . -depth -name '*)*' | while IFS= read -r f ; do mv -i "$f" "$(dirname "$f")/$(basename "$f"|tr ')' _)" ; done
find . -depth -name '* *' | while IFS= read -r f ; do mv -i "$f" "$(dirname "$f")/$(basename "$f"|tr ' ' _)" ; done