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What is Pandas Python?

Pandas is an open-source library that allows to you perform data manipulation and analysis in Python. Pandas Python library offers data manipulation and data operations for numerical tables and time series. Pandas provide an easy way to create, manipulate, and wrangle the data. It is built on top of NumPy, means it needs NumPy to operate.

In this Python Pandas tutorial, you will learn Pandas Python basics like:

Why use Pandas?

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?

Now in this Python Pandas tutorial, we will learn how to install Pandas in Python.

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 Pandas DataFrame?

Pandas DataFrame is a two-dimensional array with labelled data structure having different column types. A DataFrame is a standard way to store data in a tabular format, 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 can be the price values.

Data Frame is well known by statistician and other data practitioners.

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.

Python Pandas Series has following 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: chúng tôi artificially

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

Output

0 1.0 1 2.0 2 NaN dtype: float64 Create Pandas DataFrame

Now in this Pandas DataFrame tutorial, we will learn how to create Python Pandas dataframe:

You can convert a numpy array to a pandas data frame with chúng tôi 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

Pandas Range Data

Pandas have a convenient API to create a range of date. Let’s learn with Python Pandas examples:

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 as shown in the below Pandas example:

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 Python Pandas tutorial is about how to slice a pandas data frame.

You can use the column name to extract data in a particular column as shown in the below Pandas example:

## 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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