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What is Pandas?

Pandas is an opensource library that allows to you perform data manipulation in Python. Pandas library is built on top of Numpy, meaning Pandas needs Numpy to operate. Pandas provide an easy way to create, manipulate and wrangle the data. Pandas is also an elegant solution for time series data.

In this guide, you will learn:

Why use Pandas?

Data scientists use Pandas for its following advantages:

  • Easily handles missing data
  • It uses Series for one-dimensional data structure and DataFrame for multi-dimensional data structure
  • It provides an efficient way to slice the data
  • It provides a flexible way to merge, concatenate or reshape the data
  • It includes a powerful time series tool to work with

In a nutshell, Pandas is a useful library in data analysis. It can be used to perform data manipulation and analysis. Pandas provide powerful and easy-to-use data structures, as well as the means to quickly perform operations on these structures.

How to install Pandas?

To install Pandas library, please refer our tutorial How to install TensorFlow. Pandas is installed by default. In remote case, pandas not installed-

You can install Pandas using:

  • Anaconda: conda install -c anaconda pandas
  • In Jupyter Notebook :
import sys
!conda install --yes --prefix {sys.prefix} pandas			

What is a data frame?

A data frame is a two-dimensional array, with labeled axes (rows and columns). A data frame is a standard way to store data.

Data frame is well-known by statistician and other data practitioners. A data frame is a tabular data, with rows to store the information and columns to name the information. For instance, the price can be the name of a column and 2,3,4 the price values.

Below a picture of a Pandas data frame:

What is a Series?

A series is a one-dimensional data structure. It can have any data structure like integer, float, and string. It is useful when you want to perform computation or return a one-dimensional array. A series, by definition, cannot have multiple columns. For the latter case, please use the data frame structure.

Series has one parameters:

  • Data: can be a list, dictionary or scalar value
pd.Series([1., 2., 3.])			
0    1.0
1    2.0
2    3.0
dtype: float64			

You can add the index with index. It helps to name the rows. The length should be equal to the size of the column

pd.Series([1., 2., 3.], index=['a', 'b', 'c'])

Below, you create a Pandas series with a missing value for the third rows. Note, missing values in Python are noted "NaN." You can use numpy to create missing value: np.nan artificially

pd.Series([1,2,np.nan])

Output

0    1.0
1    2.0
2    NaN
dtype: float64

Create Data frame

You can convert a numpy array to a pandas data frame with pd.Data frame(). The opposite is also possible. To convert a pandas Data Frame to an array, you can use np.array()

## Numpy to pandas
import numpy as np
h = [[1,2],[3,4]] 
df_h = pd.DataFrame(h)
print('Data Frame:', df_h)

## Pandas to numpy
df_h_n = np.array(df_h)
print('Numpy array:', df_h_n)
Data Frame:    0  1
0  1  2
1  3  4
Numpy array: [[1 2]
 [3 4]]

You can also use a dictionary to create a Pandas dataframe.

dic = {'Name': ["John", "Smith"], 'Age': [30, 40]}
pd.DataFrame(data=dic)				
Age Name
0 30 John
1 40 Smith

Range Data

Pandas have a convenient API to create a range of date

pd.data_range(date,period,frequency):

  • The first parameter is the starting date
  • The second parameter is the number of periods (optional if the end date is specified)
  • The last parameter is the frequency: day: 'D,' month: 'M' and year: 'Y.'
## Create date
# Days
dates_d = pd.date_range('20300101', periods=6, freq='D')
print('Day:', dates_d)

Output

Day: DatetimeIndex(['2030-01-01', '2030-01-02', '2030-01-03', '2030-01-04', '2030-01-05', '2030-01-06'], dtype='datetime64[ns]', freq='D')
# Months
dates_m = pd.date_range('20300101', periods=6, freq='M')
print('Month:', dates_m)

Output

Month: DatetimeIndex(['2030-01-31', '2030-02-28', '2030-03-31', '2030-04-30','2030-05-31', '2030-06-30'], dtype='datetime64[ns]', freq='M')

Inspecting data

You can check the head or tail of the dataset with head(), or tail() preceded by the name of the panda's data frame

Step 1) Create a random sequence with numpy. The sequence has 4 columns and 6 rows

random = np.random.randn(6,4)	

Step 2) Then you create a data frame using pandas.

Use dates_m as an index for the data frame. It means each row will be given a "name" or an index, corresponding to a date.

Finally, you give a name to the 4 columns with the argument columns

# Create data with date
df = pd.DataFrame(random,
                  index=dates_m,
                  columns=list('ABCD'))

Step 3) Using head function

df.head(3)
A B C D
2030-01-31 1.139433 1.318510 -0.181334 1.615822
2030-02-28 -0.081995 -0.063582 0.857751 -0.527374
2030-03-31 -0.519179 0.080984 -1.454334 1.314947

Step 4) Using tail function

df.tail(3)
A B C D
2030-04-30 -0.685448 -0.011736 0.622172 0.104993
2030-05-31 -0.935888 -0.731787 -0.558729 0.768774
2030-06-30 1.096981 0.949180 -0.196901 -0.471556

Step 5) An excellent practice to get a clue about the data is to use describe(). It provides the counts, mean, std, min, max and percentile of the dataset.

df.describe()
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.002317 0.256928 -0.151896 0.467601
std 0.908145 0.746939 0.834664 0.908910
min -0.935888 -0.731787 -1.454334 -0.527374
25% -0.643880 -0.050621 -0.468272 -0.327419
50% -0.300587 0.034624 -0.189118 0.436883
75% 0.802237 0.732131 0.421296 1.178404
max 1.139433 1.318510 0.857751 1.615822

Slice data

The last point of this tutorial is about how to slice a pandas data frame.

You can use the column name to extract data in a particular column.

## Slice
### Using name
df['A']

2030-01-31   -0.168655
2030-02-28    0.689585
2030-03-31    0.767534
2030-04-30    0.557299
2030-05-31   -1.547836
2030-06-30    0.511551
Freq: M, Name: A, dtype: float64

To select multiple columns, you need to use two times the bracket, [[..,..]]

The first pair of bracket means you want to select columns, the second pairs of bracket tells what columns you want to return.

df[['A', 'B']]. 				
A B
2030-01-31 -0.168655 0.587590
2030-02-28 0.689585 0.998266
2030-03-31 0.767534 -0.940617
2030-04-30 0.557299 0.507350
2030-05-31 -1.547836 1.276558
2030-06-30 0.511551 1.572085

You can slice the rows with :

The code below returns the first three rows

### using a slice for row
df[0:3]	
A B C D
2030-01-31 -0.168655 0.587590 0.572301 -0.031827
2030-02-28 0.689585 0.998266 1.164690 0.475975
2030-03-31 0.767534 -0.940617 0.227255 -0.341532

The loc function is used to select columns by names. As usual, the values before the coma stand for the rows and after refer to the column. You need to use the brackets to select more than one column.

## Multi col
df.loc[:,['A','B']]	
A B
2030-01-31 -0.168655 0.587590
2030-02-28 0.689585 0.998266
2030-03-31 0.767534 -0.940617
2030-04-30 0.557299 0.507350
2030-05-31 -1.547836 1.276558
2030-06-30 0.511551 1.572085

There is another method to select multiple rows and columns in Pandas. You can use iloc[]. This method uses the index instead of the columns name. The code below returns the same data frame as above

df.iloc[:, :2]
A B
2030-01-31 -0.168655 0.587590
2030-02-28 0.689585 0.998266
2030-03-31 0.767534 -0.940617
2030-04-30 0.557299 0.507350
2030-05-31 -1.547836 1.276558
2030-06-30 0.511551 1.572085

Drop a column

You can drop columns using pd.drop()

df.drop(columns=['A', 'C'])								
B D
2030-01-31 0.587590 -0.031827
2030-02-28 0.998266 0.475975
2030-03-31 -0.940617 -0.341532
2030-04-30 0.507350 -0.296035
2030-05-31 1.276558 0.523017
2030-06-30 1.572085 -0.594772

Concatenation

You can concatenate two DataFrame in Pandas. You can use pd.concat()

First of all, you need to create two DataFrames. So far so good, you are already familiar with dataframe creation

import numpy as np
df1 = pd.DataFrame({'name': ['John', 'Smith','Paul'],
                     'Age': ['25', '30', '50']},
                    index=[0, 1, 2])
df2 = pd.DataFrame({'name': ['Adam', 'Smith' ],
                     'Age': ['26', '11']},
                    index=[3, 4])  

Finally, you concatenate the two DataFrame

df_concat = pd.concat([df1,df2]) 
df_concat
Age name
0 25 John
1 30 Smith
2 50 Paul
3 26 Adam
4 11 Smith

Drop_duplicates

If a dataset can contain duplicates information use, `drop_duplicates` is an easy to exclude duplicate rows. You can see that `df_concat` has a duplicate observation, `Smith` appears twice in the column `name.`

df_concat.drop_duplicates('name')
Age name
0 25 John
1 30 Smith
2 50 Paul
3 26 Adam

Sort values

You can sort value with sort_values

df_concat.sort_values('Age')
Age name
4 11 Smith
0 25 John
3 26 Adam
1 30 Smith
2 50 Paul

Rename: change of index

You can use rename to rename a column in Pandas. The first value is the current column name and the second value is the new column name.

df_concat.rename(columns={"name": "Surname", "Age": "Age_ppl"})
Age_ppl Surname
0 25 John
1 30 Smith
2 50 Paul
3 26 Adam
4 11 Smith

Summary

Below is a summary of the most useful method for data science with Pandas

import data read_csv
create series Series
Create Dataframe DataFrame
Create date range date_range
return head head
return tail tail
Describe describe
slice using name dataname['columnname']
Slice using rows data_name[0:5]

 

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