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https://github.com/idouble/pandas-python-data-analysis-playground
🐍 Data Analysis with the Pandas Library & Notes 📊📈
https://github.com/idouble/pandas-python-data-analysis-playground
analysis csv csv-files data data-analysis data-science data-visualization dataframe examples library pandas pandas-dataframe pandas-library pandas-python python
Last synced: 12 days ago
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🐍 Data Analysis with the Pandas Library & Notes 📊📈
- Host: GitHub
- URL: https://github.com/idouble/pandas-python-data-analysis-playground
- Owner: IDouble
- License: mit
- Created: 2019-02-10T14:46:38.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-02-29T16:58:38.000Z (11 months ago)
- Last Synced: 2024-12-24T08:25:51.339Z (18 days ago)
- Topics: analysis, csv, csv-files, data, data-analysis, data-science, data-visualization, dataframe, examples, library, pandas, pandas-dataframe, pandas-library, pandas-python, python
- Language: Python
- Homepage:
- Size: 8.93 MB
- Stars: 41
- Watchers: 4
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🐍 Pandas Python Data Analysis Playground 📊📈
🐍 Data Analysis with the Pandas Library 📊📈
## Installation Pandas ⬇️
The easiest way to install Pandas is with pip. Type in your console:
```
pip install pandas
```
## Load DataFrame from a CSV File 📂
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 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 10-20
print(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 Dateframe
print(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 Time
print(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 Dateframe
print(df.sample(10))
```
## Create Data Frame 🗂
```
# 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)
```
## Draw Candlestick Chart with moving averages 📈
```
import pandas as pd
import matplotlib.pyplot as plt
import datetime
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates
df = pd.read_csv('candlestick_chart.csv')
# ensuring only equity series is considered
df = df.loc[df['Series'] == 'EQ']
# Converting date to pandas datetime format
df['Date'] = pd.to_datetime(df['Date'])
df["Date"] = df["Date"].apply(mdates.date2num)
# Creating required data in new DataFrame OHLC
ohlc= 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 candlesticks
candlestick_ohlc(ax, ohlc.values, width=.6, colorup='green', colordown='red')
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
# Creating SMA columns
ohlc['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()
```
## Draw financial Chart 💹
```
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()
```
