Data Science Portfolio

Pandas Basics

15 May 2019

Import modules

import pandas as pd
import numpy as np

Create an empty dataframe

df = pd.DataFrame()
print(df)

Empty DataFrame
Columns: []
Index: []

Create a dataframe from list

data = [1, 2, 3, 4]
df = pd.DataFrame(data)
print(df)

   0
0  1
1  2
2  3
3  4

Create a dataframe from list of lists

data = [[]]

Create a dataframe from dict of ndarrays/lists

data = {'Student': ['Adam', 'John', 'Rachel', 'Sarah'], 'Test Score': [95, 78, 84, 91]}
df = pd.DataFrame(data)
print(df)

  Student  Test Score
0    Adam          95
1    John          78
2  Rachel          84
3   Sarah          91

Create a dataframe from list of dicts

data = [{'a': 1, 'b': 2, 'c': 3}, {'a': 5, 'b': 6, 'c': 7, 'd': 8}]
df = pd.DataFrame(data)
print(df)

   a  b  c    d
0  1  2  3  NaN
1  5  6  7  8.0

Note how NaN is appended in missing areas where no value is provided.

Selecting columns

Columns can be selected using bracket notation.

data = [{'a': 1, 'b': 2, 'c': 3}, {'a': 6, 'b': 7, 'c': 8, 'd': 9}]
df = pd.DataFrame(data)
print(df['a'])

0    1
1    6
Name: a, dtype: int64

Adding columns

New columns can be added by passing in a Series.

data = [{'a': 1, 'b': 2, 'c': 3}, {'a': 6, 'b': 7, 'c': 8, 'd': 9}]
df = pd.DataFrame(data)

# Add a new column by passing in a Series
df['e'] = pd.Series([5, 10], index=[0, 1])
print(df)

   a  b  c    d   e
0  1  2  3  NaN   5
1  6  7  8  9.0  10

Deleting columns

Columns can be deleted using either del or the pop() function.

data = [{'a': 1, 'b': 2, 'c': 3}, {'a': 6, 'b': 7, 'c': 8, 'd': 9}]
df = pd.DataFrame(data)

# Using del function
print('Deleting column \'d\' using DEL function:')
del df['d']
print(df)

# Using pop function
print('Deleting column \'c\' using POP function:')
df.pop('c')
print(df)

Deleting column 'd' using DEL function:
   a  b  c
0  1  2  3
1  6  7  8
Deleting column 'c' using POP function:
   a  b
0  1  2
1  6  7

Row selection by label

Rows can be selected using the loc() function.

data = {'Student': ['Adam', 'John', 'Rachel', 'Sarah'], 'Test Score': [95, 78, 84, 91]}
df = pd.DataFrame(data)
print(df.loc[0])

Student       Adam
Test Score      95
Name: 0, dtype: object

Row selection by integer location

Rows can be selected by their integer location using the iloc() function.

data = {'Student': ['Adam', 'John', 'Rachel', 'Sarah'], 'Test Score': [95, 78, 84, 91]}
df = pd.DataFrame(data)
print(df.iloc[2])

Student       Rachel
Test Score        84
Name: 2, dtype: object

Row selection by slicing

Slice notation involves using a range, i.e. two indexes separated by a colon.

data = {'Student': ['Adam', 'John', 'Rachel', 'Sarah'], 'Test Score': [95, 78, 84, 91]}
df = pd.DataFrame(data)
print(df[2:4])

  Student  Test Score
2  Rachel          84
3   Sarah          91

Row addition

Rows can be added with the append() function.

data = {'Student': ['Adam', 'John', 'Rachel', 'Sarah'], 'Test Score': [95, 78, 84, 91]}
data2 = {'Student': ['Michael'], 'Test Score': [97]}
df = pd.DataFrame(data)
df2 = pd.DataFrame(data2)
df = df.append(df2, ignore_index=True)
print(df)

   Student  Test Score
0     Adam          95
1     John          78
2   Rachel          84
3    Sarah          91
4  Michael          97

Row deletion

Rows can be deleted using the drop() function.

data = {'Student': ['Adam', 'John', 'Rachel', 'Sarah'], 'Test Score': [95, 78, 84, 91]}
df = pd.DataFrame(data)
df = df.drop(0)
print(df)

  Student  Test Score
1    John          78
2  Rachel          84
3   Sarah          91