> For the complete documentation index, see [llms.txt](https://gyansetu-python.gitbook.io/python-programming/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gyansetu-python.gitbook.io/python-programming/pandas.md). # Pandas *pandas* is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. We can analyze data in pandas with: 1. Series 2. DataFrames Series: ***Series*** is one dimensional(1-D) array defined in pandas that can be used to store any data type. **Code #1: *****Creating Series*** ```python # Program to create series import pandas as pd # Import Panda Library # Create series with Data, and Index a = pd.Series(Data, index = Index) ``` Here, Data can be: 1. A Scalar value which can be integerValue, string 2. A Python Dictionary which can be Key, Value pair 3. A Ndarray **Code #2: *****When Data contains scalar values*** ```python # Program to Create series with scalar values Data =[1, 3, 4, 5, 6, 2, 9] # Numeric data # Creating series with default index values s = pd.Series(Data) print(s) # predefined index values Index =['a', 'b', 'c', 'd', 'e', 'f', 'g'] # Creating series with predefined index values si = pd.Series(Data, Index) print(si) ``` **OUTPUT:** ![](/files/-Lf_Sgngk4Jgsb1OV5vc) ![](/files/-Lf_SmckYl1DtFfsoydp) **Code #3: *****When Data contains Dictionary*** ```python # Program to Create Dictionary series dictionary ={'a':1, 'b':2, 'c':3, 'd':4, 'e':5} # Creating series of Dictionary type sd = pd.Series(dictionary) print(sd) ``` **OUTPUT:** ![](/files/-Lf_T7fy-nURCyAaDTQb) **Code #4:*****When Data contains Ndarray*** ```python # Program to Create ndarray series Data =[[2, 3, 4], [5, 6, 7]] # Defining 2darray # Creating series of 2darray snd = pd.Series(Data) print(snd) ``` **OUTPUT:** ![](/files/-Lf_T_bTGwc0cFAetWiy) ```python # We import Pandas as pd into Python import pandas as pd # We create a Pandas Series that stores a grocery list groceries = pd.Series(data = [30, 6, 'Yes','No'], index = ['eggs', 'apples', 'milk', 'bread']) # We display the Groceries Pandas Series print(groceries) ``` eggs 30 apples 6 milk Yes bread No dtype: object ```python # We print some information about Groceries print('Groceries has shape:', groceries.shape) print('Groceries has dimension:', groceries.ndim) print('Groceries has a total of', groceries.size, 'elements') ``` Groceries has shape: (4,) Groceries has dimension: 1 Groceries has a total of 4 elements ```python # We print the index and data of Groceries print('The data in Groceries is:', groceries.values) print('The index of Groceries is:', groceries.index) ``` The data in Groceries is: \[30 6 'Yes' 'No'] The index of Groceries is: Index(\['eggs', 'apples', 'milk', 'bread'], dtype='object') ```python # We check whether bananas is a food item (an index) in Groceries x = 'bananas' in groceries # We check whether bread is a food item (an index) in Groceries y = 'bread' in groceries # We print the results print('Is bananas an index label in Groceries:', x) print('Is bread an index label in Groceries:', y) ``` Is bananas an index label in Groceries: False Is bread an index label in Groceries: True ```python # We access elements in Groceries using index labels: # We use a single index label print('How many eggs do we need to buy:', groceries['eggs']) print() # we can access multiple index labels print('Do we need milk and bread:\n', groceries[['milk', 'bread']]) print() ``` How many eggs do we need to buy: 30 Do we need milk and bread: milk Yes bread No dtype: object We can also delete items from a Pandas Series by using the `.drop()` method. The `Series.drop(label)` method removes the given `label` from the given `Series`. We should note that the `Series.drop(label)` method drops elements from the Series out of place, meaning that it doesn't change the original Series being modified. Let's see how this works: ``` # We display the original grocery list print('Original Grocery List:\n', groceries) # We remove apples from our grocery list. The drop function removes elements out of place print() print('We remove apples (out of place):\n', groceries.drop('apples')) # When we remove elements out of place the original Series remains intact. To see this # we display our grocery list again print() print('Grocery List after removing apples out of place:\n', groceries) ``` > Original Grocery List:\ > eggs 30\ > apples 6\ > milk Yes\ > bread No\ > dtype: object > We remove apples (out of place):\ > eggs 30\ > milk Yes\ > bread No\ > dtype: object > Grocery List after removing apples out of place:\ > eggs 30\ > apples 6\ > milk Yes\ > bread No\ > dtype: object We can delete items from a Pandas Series in place by setting the keyword `inplace` to `True` in the `.drop()` method. Let's see an example: ``` # We display the original grocery list print('Original Grocery List:\n', groceries) # We remove apples from our grocery list in place by setting the inplace keyword to True groceries.drop('apples', inplace = True) # When we remove elements in place the original Series its modified. To see this # we display our grocery list again print() print('Grocery List after removing apples in place:\n', groceries) ``` > Original Grocery List:\ > eggs 30\ > apples 6\ > milk Yes\ > bread No\ > dtype: object > Grocery List after removing apples in place:\ > eggs 30\ > milk Yes\ > bread No\ > dtype: object ## Arithmetic Operations on Pandas Series Just like with NumPy ndarrays, we can perform element-wise arithmetic operations on Pandas Series. In this lesson we will look at arithmetic operations between Pandas Series and single numbers. Let's create a new Pandas Series that will hold a grocery list of just fruits. ``` # We create a Pandas Series that stores a grocery list of just fruits fruits= pd.Series(data = [10, 6, 3,], index = ['apples', 'oranges', 'bananas']) # We display the fruits Pandas Series fruits ``` > apples 10\ > oranges 6\ > bananas 3\ > dtype: int64 We can now modify the data in fruits by performing basic arithmetic operations. Let's see some examples ``` # We print fruits for reference print('Original grocery list of fruits:\n ', fruits) # We perform basic element-wise operations using arithmetic symbols print() print('fruits + 2:\n', fruits + 2) # We add 2 to each item in fruits print() print('fruits - 2:\n', fruits - 2) # We subtract 2 to each item in fruits print() print('fruits * 2:\n', fruits * 2) # We multiply each item in fruits by 2 print() print('fruits / 2:\n', fruits / 2) # We divide each item in fruits by 2 print() ``` > Original grocery list of fruits:\ > apples 10\ > oranges 6\ > bananas 3\ > dtype: int64 > fruits + 2:\ > apples 12\ > oranges 8\ > bananas 5\ > dtype: int64 > fruits - 2:\ > apples 8\ > oranges 4\ > bananas 1\ > dtype: int64 > fruits \* 2:\ > apples 20\ > oranges 12\ > bananas 6\ > dtype: int64 > fruits / 2:\ > apples 5.0\ > oranges 3.0\ > bananas 1.5\ > dtype: float64 You can also apply mathematical functions from NumPy, such as`sqrt(x)`, to all elements of a Pandas Series. ``` # We import NumPy as np to be able to use the mathematical functions import numpy as np # We print fruits for reference print('Original grocery list of fruits:\n', fruits) # We apply different mathematical functions to all elements of fruits print() print('EXP(X) = \n', np.exp(fruits)) print() print('SQRT(X) =\n', np.sqrt(fruits)) print() print('POW(X,2) =\n',np.power(fruits,2)) # We raise all elements of fruits to the power of 2 ``` > Original grocery list of fruits:\ > apples 10\ > oranges 6\ > bananas 3\ > dtype: int64 > EXP(X) =\ > apples 22026.465795\ > oranges 403.428793\ > bananas 20.085537\ > dtype: float64 > SQRT(X) =\ > apples 3.162278\ > oranges 2.449490\ > bananas 1.732051\ > dtype: float64 > POW(X,2) =\ > apples 100\ > oranges 36\ > bananas 9\ > dtype: int64 ## **DataFrames:** **DataFrames** is two-dimensional(2-D) data structure defined in pandas which consists of rows and columns. **Code #1: *****Creation of DataFrame*** ```python # Program to Create DataFrame import pandas as pd # Import Library a = pd.DataFrame(Data) # Create DataFrame with Data ``` Here, Data can be: 1. One or more ***dictionaries*** 2. One or more ***Series*** 3. ***2D-numpy Ndarray*** **Code #2: When Data is Dictionaries** ```python # Program to Create Data Frame with two dictionaries dict1 ={'a':1, 'b':2, 'c':3, 'd':4} # Define Dictionary 1 dict2 ={'a':5, 'b':6, 'c':7, 'd':8, 'e':9} # Define Dictionary 2 Data = {'first':dict1, 'second':dict2} # Define Data with dict1 and dict2 df = pd.DataFrame(Data) # Create DataFrame print(df) ``` **OUTPUT:** ![](/files/-Lf_UD6JNMOm0Xecum2q) **Code #3: When Data is Series** ```python # Program to create Dataframe of three series import pandas as pd s1 = pd.Series([1, 3, 4, 5, 6, 2, 9]) # Define series 1 s2 = pd.Series([1.1, 3.5, 4.7, 5.8, 2.9, 9.3]) # Define series 2 s3 = pd.Series(['a', 'b', 'c', 'd', 'e']) # Define series 3 Data ={'first':s1, 'second':s2, 'third':s3} # Define Data dfseries = pd.DataFrame(Data) # Create DataFrame print(dfseries) ``` **OUTPUT:** ![](/files/-Lf_UMzGGY9BhIW8jRm1) **Code #4: When Data is 2D-numpy ndarray:** **Note**: One constraint has to be maintained while creating DataFrame of 2D arrays – Dimensions of 2D array must be same ```python # Program to create DataFrame from 2D array import pandas as pd # Import Library d1 =[[2, 3, 4], [5, 6, 7]] # Define 2d array 1 d2 =[[2, 4, 8], [1, 3, 9]] # Define 2d array 2 Data ={'first': d1, 'second': d2} # Define Data df2d = pd.DataFrame(Data) # Create DataFrame print(df2d) ``` **OUTPUT:** ![](/files/-Lf_Ub8RovdvUReGw--E)