Table of Contents

## How do I add a filter to a pivot table?

Show the top or bottom 10 items

- In the PivotTable, click the arrow.
- Right-click an item in the selection, and then click Filter > Top 10 or Bottom 10.
- In the first box, enter a number.
- In the second box, pick the option you want to filter by.
- In the search box, you can optionally search for a particular value.

### How do I apply a panda Filter in Excel?

First, read in the Excel file and add a column with the 2% default rate:

- import pandas as pd df = pd.
- df.
- df[“bonus”] = 0 df.
- # Calculate the compensation for each row df[“comp”] = df[“commission”] * df[“ext price”] + df[“bonus”] # Summarize and round the results by sales rep df.

#### How do I add a filter to a Dataframe in Python?

One way to filter by rows in Pandas is to use boolean expression. We first create a boolean variable by taking the column of interest and checking if its value equals to the specific value that we want to select/keep. For example, let us filter the dataframe or subset the dataframe based on year’s value 2002.

**How do I create a pivot table using pandas?**

Do my standard imports, read in the data and create my pivot table:

- import pandas as pd import numpy as np df = pd. read_excel(“sales-funnel.xlsx”) table = pd.
- for manager in table. index.
- writer = pd. ExcelWriter(‘output.xlsx’) for manager in table.

**Is NaN same as null Python?**

When it comes to data wrangling, dealing with missing values is an inevitable task. Unlike other popular programming languages, such as Java and C++, Python does not use the NULL keyword. Instead, Python uses NaN and None .

## Why does NaN float?

NaN stands for Not A Number and is a common missing data representation. It is a special floating-point value and cannot be converted to any other type than float. NaN can be seen like some sort of data virus that infects all operations it touches.

### How do you check if a value is null in pandas?

Checking for missing values using isnull() In order to check null values in Pandas DataFrame, we use isnull() function this function return dataframe of Boolean values which are True for NaN values.

#### How do I find null rows in pandas?

Use pandas. DataFrame. isnull() to find rows with NaN values

- print(df)
- is_NaN = df. isnull()
- row_has_NaN = is_NaN. any(axis=1)
- rows_with_NaN = df[row_has_NaN]
- print(rows_with_NaN)

**How are missing values calculated in pandas?**

How to Count NaN values in Pandas DataFrame

- (1) Count NaN values under a single DataFrame column: df[‘column name’].isna().sum()
- (2) Count NaN values under an entire DataFrame: df.isna().sum().sum()
- (3) Count NaN values across a single DataFrame row: df.loc[[index value]].isna().sum().sum()

**How do I count the number of values in a column in pandas?**

First, we will create a data frame, and then we will count the values of different attributes.

- Syntax: DataFrame.count(axis=0, level=None, numeric_only=False)
- Parameters:
- Returns: It returns count of non-null values and if level is used it returns dataframe.

## How do I add two columns in pandas?

Select each column of DataFrame df through the syntax df[“column_name”] and add them together to get a pandas Series containing the sum of each row. Create a new column in the DataFrame through the syntax df[“new_column”] and set it equal to this Series to add it to the DataFrame.

### What is forward fill pandas?

ffill() function is used to fill the missing value in the dataframe. ‘ffill’ stands for ‘forward fill’ and will propagate last valid observation forward.

#### How do you fill in pandas?

How to fill a Pandas DataFrame row by row in Python

- Use pandas. DataFrame. loc to build a Pandas DataFrame row by row.
- Use pandas. DataFrame. append() to append a new row to a Pandas DataFrame.
- Use a list of lists to build a Pandas DataFrame row by row. Pandas DataFrames can be built through a list of lists, where each sublist is a row in the DataFrame.

**What is fill forward?**

Forward filling and backward filling are two approaches to fill missing values. Forward filling means fill missing values with previous data. Backward filling means fill missing values with next data point.

**What is Ffill and bfill in pandas?**

bfill() is used to backward fill the missing values in the dataset. ffill() function is used forward fill the missing value in the dataframe. So this recipe is a short example on What is ffill and bfill in pandas. Let’s get started.

## What does bfill do in pandas?

bfill() is used to backward fill the missing values in the dataset. It will backward fill the NaN values that are present in the pandas dataframe.

### How do you use Skipna in pandas?

We use the default value of skipna parameter i.e. skipna=True to find the mean of DataFrame along the specified axis ignoring NaN values. If we set skipna=True , it ignores the NaN in the dataframe. It allows us to calculate the mean of DataFrame along column axis ignoring NaN values.

#### How do pandas deal with missing dates?

ndex:

- port pandas as pd.
- idx = pd.date_range(’09-01-2013′, ’09-30-2013′)
- s = pd.Series({’09-02-2013′: 2,
- ’09-03-2013′: 10,
- ’09-06-2013′: 5,
- ’09-07-2013′: 1})
- s.index = pd.DatetimeIndex(s.index)
- s = s.reindex(idx, fill_value=0)

**How do you deal with missing dates in time series?**

In time series data, if there are missing values, there are two ways to deal with the incomplete data:

- omit the entire record that contains information.
- Impute the missing information.

**How does Python handle missing dates?**

How to deal with missing values in a Timeseries in Python?

- Step 1 – Import the library. import pandas as pd import numpy as np.
- Step 2 – Setting up the Data. We have created a dataframe with index as timeseries and with a feature “sales”.
- Step 3 – Dealing with missing values. Here we will be using different methods to deal with missing values.

## How is NaN data treated?

5 simple ways to deal with NaN in your data

- Dropping only the null values row-wise. Some times you just need to drop a few rows that contain null values.
- Filling the null values with a value.
- Filling the cell containing NaN values with previous entry.
- Iterating through a column & doing operation on Non NaN.