Time Series Visualization in Python

Importing Libraries

In [1]:
import pandas as pd
from pandas import DataFrame, Series
import numpy as np
import matplotlib.pyplot as plt
import seaborn;

%matplotlib inline

Reading data

In [2]:
date_parser = lambda date: pd.datetime.strptime(date, '%d/%m/%Y')


df = pd.read_csv('../data/startup_funding.csv', date_parser=date_parser, parse_dates=['Date'], 
                 thousands=',')
df.drop(['SNo'], axis=1, inplace=True)

Data headshot

In [3]:
df.head()
Out[3]:
Date StartupName IndustryVertical SubVertical CityLocation InvestorsName InvestmentType AmountInUSD Remarks
0 2017-08-01 TouchKin Technology Predictive Care Platform Bangalore Kae Capital Private Equity 1300000.0 NaN
1 2017-08-02 Ethinos Technology Digital Marketing Agency Mumbai Triton Investment Advisors Private Equity NaN NaN
2 2017-08-02 Leverage Edu Consumer Internet Online platform for Higher Education Services New Delhi Kashyap Deorah, Anand Sankeshwar, Deepak Jain,… Seed Funding NaN NaN
3 2017-08-02 Zepo Consumer Internet DIY Ecommerce platform Mumbai Kunal Shah, LetsVenture, Anupam Mittal, Hetal … Seed Funding 500000.0 NaN
4 2017-08-02 Click2Clinic Consumer Internet healthcare service aggregator Hyderabad Narottam Thudi, Shireesh Palle Seed Funding 850000.0 NaN
In [4]:
fig = plt.figure(figsize=(16, 8))
fig.suptitle('Investment over time', fontsize=24, fontweight='bold')
ax = fig.add_subplot(211)
ts1 = df.groupby([df['Date'].dt.year, df['Date'].dt.quarter]).agg({'AmountInUSD':'sum'})['AmountInUSD']
ts1.plot(linewidth=4, marker="o", markersize=15, markerfacecolor='green')
plt.ylabel('USD in Billions')
plt.xlabel('Year-Quarter No.')
# plt.title('Total Investment over time')

ax = fig.add_subplot(212)
ts1 = df.groupby([df['Date'].dt.year, df['Date'].dt.month]).agg({'AmountInUSD':'sum'})['AmountInUSD']
ts1.plot(linewidth=4, marker="o", markersize=15, markerfacecolor='green')
plt.ylabel('USD in Billions')
plt.xlabel('Year-Month')
Out[4]:
<matplotlib.text.Text at 0xd41bcc0>

Grouping by index of dataframe

In [5]:
df = df.set_index(['Date'])

df.head()
Out[5]:
StartupName IndustryVertical SubVertical CityLocation InvestorsName InvestmentType AmountInUSD Remarks
Date
2017-08-01 TouchKin Technology Predictive Care Platform Bangalore Kae Capital Private Equity 1300000.0 NaN
2017-08-02 Ethinos Technology Digital Marketing Agency Mumbai Triton Investment Advisors Private Equity NaN NaN
2017-08-02 Leverage Edu Consumer Internet Online platform for Higher Education Services New Delhi Kashyap Deorah, Anand Sankeshwar, Deepak Jain,… Seed Funding NaN NaN
2017-08-02 Zepo Consumer Internet DIY Ecommerce platform Mumbai Kunal Shah, LetsVenture, Anupam Mittal, Hetal … Seed Funding 500000.0 NaN
2017-08-02 Click2Clinic Consumer Internet healthcare service aggregator Hyderabad Narottam Thudi, Shireesh Palle Seed Funding 850000.0 NaN
In [6]:
ts = df.groupby(['Date']).agg({'AmountInUSD':'sum'})['AmountInUSD']
In [7]:
ts.head()
Out[7]:
Date
2015-01-02     2465000.0
2015-01-05    10515000.0
2015-01-06    11885000.0
2015-01-07     5155768.0
2015-01-09    13000000.0
Name: AmountInUSD, dtype: float64