🐍 Data Analysis with the Pandas Library 📊📈
The easiest way to install Pandas is with pip. Type in your console:
pip install pandas Load a DateFrame from a CSV File. (Method .read_csv("your_csv_file.csv"))
import pandas as pd df = pd.read_csv("new_york_city.csv") Print 10 Rows from a Dateframe using an Integer Index from 10-20. (Method .iloc[from:to])
# Print 10 Rows from Dateframe with Integer Index from 10-20 print(df.iloc[10:20]) Print the first 10 Rows from a Dateframe. (Method .head(amount))
# Print the first 10 Rows from the Dateframe print(df.head(10)) Print 10 Rows from a Dateframe using an Integer Index from 0-10 and sort them with an attribute. (Method .sort_values(["Start Time"]))
# Prints the first 10 Rows, sorted by Start Time print(df.iloc[0:10].sort_values(["Start Time"])) Print 10 random Rows from a Dateframe. (Method .sample(amount))
# Print 10 random Rows from a Dateframe print(df.sample(10)) # Create data for the Data Frame data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2002, 2001, 2002, 2003], 'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]} # Create Data Frame df = pd.DataFrame(data) 
import pandas as pd import matplotlib.pyplot as plt import matplotlib from datetime import datetime fig = plt.figure() ax = fig.add_subplot(1, 1, 1) data = pd.read_csv('spx.csv', index_col=0, parse_dates=True) spx = data['SPX'] spx.plot(ax=ax, style='k-') crisis_data = [ (datetime(2007, 10, 11), 'Peak of bull market'), (datetime(2008, 3, 12), 'Bear Stearns Fails'), (datetime(2008, 9, 15), 'Lehman Bankruptcy') ] for date, label in crisis_data: ax.annotate(label, xy=(date, spx.asof(date) + 75), xytext=(date, spx.asof(date) + 225), arrowprops=dict(facecolor='black', headwidth=4, width=2, headlength=4), horizontalalignment='left', verticalalignment='top') # Zoom in on 2007-2010 ax.set_xlim(['1/1/2007', '1/1/2011']) ax.set_ylim([600, 1800]) ax.set_title('Important dates in the 2008-2009 financial crisis') fig.show()