Pandas Practice Code (Set - 1)

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Series([], dtype: float64)

<ipython-input-1-c46511ad4fca>:12: DeprecationWarning: The default dtype f

or empty Series will be 'object' instead of 'float64' in a future version.

Specify a dtype explicitly to silence this warning.

my_series = pd.Series()

0 10

1 20

2 30

3 40

4 50

dtype: int32

# A Pandas series is a data structure that stores data in the form

# of a column. A series is normally used to store information

# about a particular attribute in your dataset. Let’s see how you

# can create a series in Pandas.

# There are different ways to create a series with Pandas. The

# following script imports the Pandas module and then calls the

# Series() class constructor to create an empty series. Here is

# how to do that:

# empty series

import pandas as pd

my_series = pd.Series()

print(my_series)

# You can also create a series using a NumPy array. But, first,

# you need to pass the array to the Series() class constructor, as

# shown in the script below.

# series using numpy array

import pandas as pd

import numpy as np

my_array = np.array([10, 20, 30, 40, 50])

my_series = pd.Series(my_array)

print(my_series)

# In the output, you can see that the indexes for a series

# start from 0 to 1 less than the number of items in the series.

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num1 10

num2 20

num3 30

num4 40

num5 50

dtype: int32

num1 10

num2 20

num3 30

num4 40

num5 50

dtype: int64

num1 25

num2 25

num3 25

num4 25

num5 25

dtype: int64

# You can also define custom indexes for your series. To do so,

# you need to pass your list of indexes to the index attribute of

# the Series class, as shown in the script below:

# series with custom indexes

import pandas as pd

import numpy as np

my_array = np.array([10, 20, 30, 40, 50])

my_series = pd.Series(my_array, index = ["num1", "num2", "num3", "num4", "num5"])

print(my_series)

# You can also create a series by directly passing a Python list

# to the Series() class constructor.

# series using a list

import pandas as pd

import numpy as np

my_series = pd.Series([10, 20, 30, 40, 50], index = ["num1", "num2", "num3", "num4", "nu

print(my_series)

# Finally, a scaler value can also be used to define a series. In

# case you pass a list of indexes, the scaler value will be

# repeated the number of times equal to the items in the index

# list. Here is an example:

# series using a scaler

import pandas as pd

import numpy as np

my_series = pd.Series(25, index = ["num1", "num2", "num3", "num4", "num5"])

print(my_series)

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num1 6

num2 7

num3 8

dtype: int64

10

30

2

11

# Finally, you can also create a series using a dictionary. In this

# case, the dictionary keys will become series indexes while the

# dictionary values are inserted as series items. Here is an

# example:

# series using dictionary

import pandas as pd

import numpy as np

my_dict = {'num1' :6,

'num2' :7,

'num3' :8}

my_series = pd.Series(my_dict)

print(my_series)

# Let’s see some of the useful operations you can perform with

# the Pandas series.

# You can use square brackets as well as index labels to access

# series items, as shown in the following script:

## Accessing Items

import pandas as pd

my_series = pd.Series([10, 20, 30, 40, 50], index = ["num1", "num2", "num3", "num4", "nu

print(my_series[0])

print(my_series['num3'])

# Using the min() and max() functions from the NumPy module,

# you can find the maximum and minimum values, respectively,

# from a series. Look at the following script for reference.

# Finding Maximum and Minimum Values

import pandas as pd

import numpy as np

my_series = pd.Series([5, 8, 2, 11, 9])

print(np.min(my_series))

print(np.max(my_series))

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7.0

8.0

int64

# Similarly, the mean() method from the NumPy module can

# find the mean of a Pandas series, as shown in the following

# script.

# Finding Mean

import pandas as pd

import numpy as np

my_series = pd.Series([5, 8, 2, 11, 9])

print(my_series.mean())

# The following script finds the median value of a Pandas series.

# Finding Median

import pandas as pd

import numpy as np

my_series = pd.Series([5, 8, 2, 11, 9])

print(my_series.median())

# You can also find the data type of a Pandas series using the

# dtype attribute. Here is an example:

## Finding Data Type

import pandas as pd

import numpy as np

my_series = pd.Series([5, 8, 2, 11, 9])

print(my_series.dtype)