Getting Started¶
# Conventional way to import pandas
import pandas as pd
# Check pandas version
pd.__version__
'1.1.2'
# Show version of all packages
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-64-generic
Version : #58-Ubuntu SMP Fri Jul 10 19:33:51 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 47.3.1
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: 0.9.0
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
Creating Series¶
# Create Series
s1 = pd.Series([3, 6, 9, 12])
s1
0 3
1 6
2 9
3 12
dtype: int64
# Check type
type(s1)
pandas.core.series.Series
# To see values
s1.values
array([ 3, 6, 9, 12])
# To see index/keys
s1.index
RangeIndex(start=0, stop=4, step=1)
# Creating labeled series
s2 = pd.Series([200000, 300000, 4000000, 500000], index=['A', 'B', 'C', 'D'])
s2
A 200000
B 300000
C 4000000
D 500000
dtype: int64
s2.values
array([ 200000, 300000, 4000000, 500000])
s2.index
Index(['A', 'B', 'C', 'D'], dtype='object')
# Indexing
s2['A']
200000
# Boolean indexing
s2[s2 > 700000]
C 4000000
dtype: int64
Creating DataFrame¶
# Create a DataFrame
data = {'Country': ['Belgium', 'India', 'Brazil'],
'Capital': ['Brussels', 'New Delhi', 'Brasília'],
'Population': [11190846, 1303171035, 207847528]
}
df = pd.DataFrame(data, columns=["Country", "Capital", "Population"])
df
| Country | Capital | Population | |
|---|---|---|---|
| 0 | Belgium | Brussels | 11190846 |
| 1 | India | New Delhi | 1303171035 |
| 2 | Brazil | Brasília | 207847528 |
# Check type
type(df)
pandas.core.frame.DataFrame
# Indexing
df["Country"]
0 Belgium
1 India
2 Brazil
Name: Country, dtype: object
# or
df.Country
0 Belgium
1 India
2 Brazil
Name: Country, dtype: object
# Boolean indexing
df["Population"] > 40000000
0 False
1 True
2 True
Name: Population, dtype: bool
df["Country"] == "Belgium"
0 True
1 False
2 False
Name: Country, dtype: bool
df["Capital"] == "Brasilia"
0 False
1 False
2 False
Name: Capital, dtype: bool