🐍 Pandas Python Data Analysis Playground 📊📈 🐍 Data Analysis with the Pandas Library 📊📈
The easiest way to install Pandas is with pip. Type in your console:
Load DataFrame from a CSV File 📂 Load a DateFrame from a CSV File. (Method .read_csv("your_csv_file.csv"))
import pandas as pddf = pd.read_csv("new_york_city.csv")Print Rows from a Dateframe using an Integer Index 🗃 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 10-20print(df.iloc[10:20])Print the first Rows from a Dateframe 🗃 Print the first 10 Rows from a Dateframe. (Method .head(amount))
# Print the first 10 Rows from the Dateframeprint(df.head(10))Print Rows from a Dateframe and sort them with an attribute 🗃 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 Timeprint(df.iloc[0:10].sort_values(["Start Time"]))Print 10 random Rows from a Dateframe 🗃 Print 10 random Rows from a Dateframe. (Method .sample(amount))
# Print 10 random Rows from a Dateframeprint(df.sample(10))# Create data for the Data Framedata = {'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 Framedf = pd.DataFrame(data)Draw Candlestick Chart with moving averages 📈
import pandas as pdimport matplotlib.pyplot as pltimport datetimefrom mpl_finance import candlestick_ohlcimport matplotlib.dates as mdatesdf = pd.read_csv('candlestick_chart.csv')# ensuring only equity series is considereddf = df.loc[df['Series'] == 'EQ']# Converting date to pandas datetime formatdf['Date'] = pd.to_datetime(df['Date'])df["Date"] = df["Date"].apply(mdates.date2num)# Creating required data in new DataFrame OHLCohlc= df[['Date', 'Open Price', 'High Price', 'Low Price','Close Price']].copy()# In case you want to check for shorter timespan# ohlc =ohlc.tail(60)# ohlc['SMA50'] = ohlc["Close Price"].rolling(50).mean()f1, ax = plt.subplots(figsize = (10,5))# plot the candlestickscandlestick_ohlc(ax, ohlc.values, width=.6, colorup='green', colordown='red')ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))# Creating SMA columnsohlc['SMA5'] = ohlc["Close Price"].rolling(5).mean()ohlc['SMA10'] = ohlc["Close Price"].rolling(10).mean()ohlc['SMA20'] = ohlc["Close Price"].rolling(20).mean()ohlc['SMA50'] = ohlc["Close Price"].rolling(50).mean()ohlc['SMA100'] = ohlc["Close Price"].rolling(100).mean()ohlc['SMA200'] = ohlc["Close Price"].rolling(200).mean()# Plotting SMA columns# ax.plot(ohlc['Date'], ohlc['SMA5'], color = 'blue', label = 'SMA5')# ax.plot(ohlc['Date'], ohlc['SMA10'], color = 'blue', label = 'SMA10')# ax.plot(ohlc['Date'], ohlc['SMA20'], color = 'red', label = 'SMA20')ax.plot(ohlc['Date'], ohlc['SMA50'], color = 'green', label = 'SMA50')# ax.plot(ohlc.index, df['SMA100'], color = 'blue', label = 'SMA100')ax.plot(ohlc['Date'], ohlc['SMA200'], color = 'blue', label = 'SMA200')plt.show()
import pandas as pdimport matplotlib.pyplot as pltimport matplotlibfrom datetime import datetimefig = 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 2007-2010ax.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()
