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Update viewing_data_in_pandas.md
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**View the top rows of the frame**
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# View the top rows of the frame
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**Pandas Dataframe/Series.head() method**
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The pandas library in Python provides a convenient method called head() that allows you to view the first few rows of a DataFrame. Let me explain how it works:
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# Pandas Dataframe/Series.head() method:
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The pandas library in Python provides a convenient method called head() that allows you to view the first few rows of a DataFrame. Let me explain how it works:
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* The head() function returns the first n rows of a DataFrame or Series.
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* By default, it displays the first 5 rows, but you can specify a different number of rows using the n parameter.
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**Syntax**:
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dataframe.head(n)
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n is the Optional value. The number of rows to return. Default value is 5.
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**Example** :
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import pandas as pd
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df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion','tiger','rabit','dog','fox','monkey','elephant']})
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df.head(n=5)
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**Output**:
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'alligator',
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'bee',
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'falcon',
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'lion',
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'tiger'
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# View the bottom rows of the frame
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# Pandas Dataframe/Series.tail() method:
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The tail function in Python displays the last five rows of the dataframe by default. It takes in a single parameter: the number of rows. We can use this parameter to display the number of rows of our choice.
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* The tail() function returns the last n rows of a DataFrame or Series.
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* By default, it displays the last 5 rows, but you can specify a different number of rows using the n parameter.
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**Syntax**:
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dataframe.tail(n)
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n is the Optional value. The number of rows to return. Default value is 5.
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**Example** :
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import pandas as pd
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df = pd.DataFrame({'fruits': ['mongo', 'orange', 'apple', 'lemon','banana','water melon','papaya','grapes','cherry','coconut']})
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df.tail(n=5)
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**Output**:
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'water melon',
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'papaya',
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'grapes',
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'cherry',
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'coconut'
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# View basic statistical details
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# Pandas DataFrame describe() Method
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Pandas describe() is used to view some basic statistical details like percentile, mean, std, etc. of a data frame or a series of numeric values.Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
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Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types.The output will vary depending on what is provided.
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**Syntax**:
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DataFrame.describe(percentiles=None, include=None, exclude=None)
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**percentiles** : list-like of numbers, optional
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The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
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**include** :‘all’, list-like of dtypes or None (default), optional
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A list of data types to include in the result.
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* all’ : All columns of the input will be included in the output.
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* A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To select pandas categorical columns, use 'category'
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* None (default) : The result will include all numeric columns.
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**exclude** : list-like of dtypes or None (default), optional.
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A black list of data types to omit from the result.
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* A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object.
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* None (default) : The result will exclude nothing.
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**Example** :
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Describing a numeric Series.
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import pandas as pd
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s = pd.Series([1, 2, 3])
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s.describe()
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**Output** :
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count 3.0
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mean 2.0
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std 1.0
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min 1.0
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25% 1.5
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50% 2.0
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75% 2.5
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max 3.0
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dtype: float64
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**Example** :
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Describing a categorical Series.
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import pandas as pd
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s = pd.Series(['a', 'a', 'b', 'c'])
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s.describe()
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**Output** :
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count 4
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unique 3
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top a
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freq 2
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dtype: object
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*The head() function returns the first n rows of a DataFrame or Series.
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*By default, it displays the first 5 rows, but you can specify a different number of rows using the n parameter.
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** Example**
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