Open in molab View on GitHub Download .py

Introduction to Pandas¶

Written by Luke Chang & Jin Cheong

Analyzing data requires being facile with manipulating and transforming datasets to be able to test specific hypotheses. Data come in all different types of flavors and there are many different tools in the Python ecosystem to work with pretty much any type of data you might encounter. For example, you might be interested in working with functional neuroimaging data that is four dimensional. Three dimensional matrices contain brain activations in space, and these data can change over time in the 4th dimension. This type of data is well suited for numpy and specialized brain imaging packages such as nilearn. The majority of data, however, is typically in some version of a two-dimensional observations by features format as might be seen in an excel spreadsheet, a SQL table, or in a comma delimited format (i.e., csv).

In Python, the Pandas library is a powerful tool to work with this type of data. This is a very large library with a tremendous amount of functionality. In this tutorial, we will cover the basics of how to load and manipulate data and will focus on common to data munging tasks.

For those interested in diving deeper into Pandas, there are many online resources. There is the Pandas online documention, stackoverflow, and medium blogposts. I highly recommend Jake Vanderplas's terrific Python Data Science Handbook. In addition, here is a brief video by Tal Yarkoni providing a useful introduction to pandas.

After the tutorial you will have the chance to apply the methods to a new set of data.

Pandas Objects¶

Pandas has several objects that are commonly used (i.e., Series, DataFrame, Index). At it's core, Pandas Objects are enhanced numpy arrays where columns and rows can have special names and there are lots of methods to operate on the data. See Jake Vanderplas's tutorial for a more in depth overview.

Series¶

A pandas Series is a one-dimensional array of indexed data.

import pandas as pd

data = pd.Series([1, 2, 3, 4, 5])
data

	0
0	1
1	2
2	3
3	4
4	5

The indices can be integers like in the example above. Alternatively, the indices can be labels.

data_1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
data_1

	0
a	1
b	2
c	3

Also, Series can be easily created from dictionaries

data_2 = pd.Series({'A': 5, 'B': 3, 'C': 1})
data_2

	0
A	5
B	3
C	1

DataFrame¶

If a Series is a one-dimensional indexed array, the DataFrame is a two-dimensional indexed array. It can be thought of as a collection of Series objects, where each Series represents a column, or as an enhanced 2D numpy array.

In a DataFrame, the index refers to labels for each row, while columns describe each column.

First, let's create a DataFrame using random numbers generated from numpy.

import numpy as np
data_3 = pd.DataFrame(np.random.random((5, 3)))
data_3

	0	1	2
0	0.663776	0.376618	0.397256
1	0.617282	0.012529	0.582649
2	0.240806	0.745354	0.555138
3	0.507141	0.416925	0.894476
4	0.143019	0.898891	0.612962

We could also initialize with column names

data_4 = pd.DataFrame(np.random.random((5, 3)), columns=['A', 'B', 'C'])
data_4

	A	B	C
0	0.275455	0.254226	0.872723
1	0.748198	0.636870	0.171168
2	0.688998	0.810688	0.901967
3	0.934223	0.256054	0.121445
4	0.715485	0.289445	0.557504

Alternatively, we could create a DataFrame from multiple Series objects.

a = pd.Series([1, 2, 3, 4])
b = pd.Series(['a', 'b', 'c', 'd'])
data_5 = pd.DataFrame({'Numbers': a, 'Letters': b})
data_5

	Numbers	Letters
0	1	a
1	2	b
2	3	c
3	4	d

Or a python dictionary

data_6 = pd.DataFrame({'State': ['California', 'Colorado', 'New Hampshire'], 'Capital': ['Sacremento', 'Denver', 'Concord']})
data_6

	State	Capital
0	California	Sacremento
1	Colorado	Denver
2	New Hampshire	Concord

Loading Data¶

Loading data is fairly straightfoward in Pandas. Type pd.read then press tab to see a list of functions that can load specific file formats such as: csv, excel, spss, and sql.

In this example, we will use pd.read_csv to load a .csv file into a dataframe. Note that read_csv() has many options that can be used to make sure you load the data correctly. You can explore the docstrings for a function to get more information about the inputs and general useage guidelines by running pd.read_csv?

To load a csv file we will need to specify either the relative or absolute path to the file.

The command pwd will print the path of the current working directory.

pwd

NameError: name 'pwd' is not defined

Traceback (most recent call last):
  File "", line 1, in <module>
    pwd
NameError: name 'pwd' is not defined. Did you mean: 'pd'? Or did you forget to import 'pwd'?

We will now load the Pandas has many ways to read data different data formats into a dataframe. Here we will use the pd.read_csv function.

df = pd.read_csv('https://raw.githubusercontent.com/ljchang/dartbrains/master/data/salary/salary.csv', sep = ',')

Ways to check the dataframe¶

There are many ways to examine your dataframe. One easy way is to just call the dataframe variable itself.

df

	salary	gender	departm	years	age	publications
0	86285	0	bio	26.0	64.0	72
1	77125	0	bio	28.0	58.0	43
2	71922	0	bio	10.0	38.0	23
3	70499	0	bio	16.0	46.0	64
4	66624	0	bio	11.0	41.0	23
...	...	...	...	...	...	...
72	53662	1	neuro	1.0	31.0	3
73	57185	1	stat	9.0	39.0	7
74	52254	1	stat	2.0	32.0	9
75	61885	1	math	23.0	60.0	9
76	49542	1	math	3.0	33.0	5

77 rows × 6 columns

However, often the dataframes can be large and we may be only interested in seeing the first few rows. df.head() is useful for this purpose.

df.head()

	salary	departm	years	age	publications
0	86285	bio	26.0	64.0	72
1	77125	bio	28.0	58.0	43
2	71922	bio	10.0	38.0	23
3	70499	bio	16.0	46.0	64
4	66624	bio	11.0	41.0	23

On the top row, you have column names, that can be called like a dictionary (a dataframe can be essentially thought of as a dictionary with column names as the keys). The left most column (0,1,2,3,4...) is called the index of the dataframe. The default index is sequential integers, but it can be set to anything as long as each row is unique (e.g., subject IDs)

print("Indexes")
print(df.index)
print("Columns")
print(df.columns)
print("Columns are like keys of a dictionary")
print(df.keys())

Indexes
RangeIndex(start=0, stop=77, step=1)
Columns
Index(['salary', 'gender', 'departm', 'years', 'age', 'publications'], dtype='str')
Columns are like keys of a dictionary
Index(['salary', 'gender', 'departm', 'years', 'age', 'publications'], dtype='str')

You can access the values of a column by calling it directly. Single bracket returns a Series and double bracket returns a dataframe.

Let's return the first 10 rows of salary.

df['salary'][:10]

	salary
0	86285
1	77125
2	71922
3	70499
4	66624
5	64451
6	64366
7	59344
8	58560
9	58294

shape is another useful method for getting the dimensions of the matrix.

We will print the number of rows and columns in this data set using fstring formatting. First, you need to specify a string starting with 'f', like this f'anything'. It is easy to insert variables with curly brackets like this f'rows: {rows}'.

Here is more info about formatting text.

rows, cols = df.shape
print(f'There are {rows} rows and {cols} columns in this data set')

There are 77 rows and 6 columns in this data set

Describing the data¶

We can use the .describe() method to get a quick summary of the continuous values of the data frame. We will .transpose() the output to make it slightly easier to read.

df.describe().transpose()

	count	mean	std	min	25%	50%	75%	max
salary	77.0	67748.519481	15100.581435	44687.0	57185.0	62607.0	75382.0	112800.0
gender	77.0	0.142857	0.387783	0.0	0.0	0.0	0.0	2.0
years	76.0	14.973684	8.617770	1.0	8.0	14.0	23.0	34.0
age	76.0	45.486842	9.005914	31.0	38.0	44.0	53.0	65.0
publications	77.0	21.831169	15.240530	3.0	9.0	19.0	33.0	72.0

We can also get quick summary of a pandas series, or specific column of a pandas dataframe.

df.departm.describe()

	departm
count	77
unique	7
top	bio
freq	16

Sometimes, you will want to know how many data points are associated with a specific variable for categorical data. The value_counts method can be used for this goal.

For example, how many males and females are in this dataset?

df['gender'].value_counts()

	count
gender
0	67
1	9
2	1

You can see that there are more than 2 genders specified in our data.

This is likely an error in the data collection process. It's always up to the data analyst to decide what to do in these cases. Because we don't know what the true value should have been, let's just remove the row from the dataframe by finding all rows that are not '2'.

df_1 = df.loc[df['gender'] != 2]
df_1['gender'].value_counts()

	count
gender
0	67
1	9

Dealing with missing values¶

Data are always messy and often have lots of missing values. There are many different ways, in which missing data might present NaN, None, or NA, Sometimes researchers code missing values with specific numeric codes such as 999999. It is important to find these as they can screw up your analyses if they are hiding in your data.

If the missing values are using a standard pandas or numpy value such as NaN, None, or NA, we can identify where the missing values are as booleans using the isnull() method.

The isnull() method will return a dataframe with True/False values on whether a datapoint is null or not a number (nan).

df_1.isnull()

	salary	gender	departm	years	age	publications
0	False	False	False	False	False	False
1	False	False	False	False	False	False
2	False	False	False	False	False	False
3	False	False	False	False	False	False
4	False	False	False	False	False	False
...	...	...	...	...	...	...
72	False	False	False	False	False	False
73	False	False	False	False	False	False
74	False	False	False	False	False	False
75	False	False	False	False	False	False
76	False	False	False	False	False	False

76 rows × 6 columns

Suppose we wanted to count the number of missing values for each column in the dataset.

One thing that is nice about Python is that you can chain commands, which means that the output of one method can be the input into the next method. This allows us to write intuitive and concise code. Notice how we take the sum() of all of the null cases. We can chain the .null() and .sum() methods to see how many null values are added up in each column.

df_1.isnull().sum()

	0
salary	0
gender	0
departm	0
years	1
age	1
publications	0

You can use the boolean indexing once again to see the datapoints that have missing values. We chained the method .any() which will check if there are any True values for a given axis. Axis=0 indicates rows, while Axis=1 indicates columns. So here we are creating a boolean index for row where any column has a missing value.

df_1[df_1.isnull().any(axis=1)]

	salary	gender	departm	years	age	publications
18	64762	0	chem	25.0	NaN	29
24	104828	0	geol	NaN	50.0	44

You may look at where the values are not null. Note that indexes 18, and 24 are missing.

df_1[~df_1.isnull().any(axis=1)]

	salary	gender	departm	years	age	publications
0	86285	0	bio	26.0	64.0	72
1	77125	0	bio	28.0	58.0	43
2	71922	0	bio	10.0	38.0	23
3	70499	0	bio	16.0	46.0	64
4	66624	0	bio	11.0	41.0	23
...	...	...	...	...	...	...
72	53662	1	neuro	1.0	31.0	3
73	57185	1	stat	9.0	39.0	7
74	52254	1	stat	2.0	32.0	9
75	61885	1	math	23.0	60.0	9
76	49542	1	math	3.0	33.0	5

74 rows × 6 columns

There are different techniques for dealing with missing data. An easy one is to simply remove rows that have any missing values using the dropna() method.

df_1.dropna(inplace=True)

Now we can check to make sure the missing rows are removed. Let's also check the new dimensions of the dataframe.

rows_1, cols_1 = df_1.shape
print(f'There are {rows_1} rows and {cols_1} columns in this data set')
df_1.isnull().sum()

There are 74 rows and 6 columns in this data set

	0
salary	0
gender	0
departm	0
years	0
age	0
publications	0

Create New Columns¶

You can create new columns to fit your needs. For instance you can set initialize a new column with zeros.

df_1['pubperyear'] = 0

Here we can create a new column pubperyear, which is the ratio of the number of papers published per year

df_1['pubperyear'] = df_1['publications'] / df_1['years']

Indexing and slicing Data¶

Indexing in Pandas can be tricky. There are many ways to index in pandas, for this tutorial we will focus on four: loc, iloc, boolean, and indexing numpy values. For a more in depth overview see Jake Vanderplas's tutorial](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.02-Data-Indexing-and-Selection.ipynb), where he also covers more advanced topics, such as hierarchical indexing.

Indexing with Keys¶

First, we will cover indexing with keys using the .loc method. This method references the explicit index with a key name. It works for both index names and also column names. Note that often the keys for rows are integers by default.

In this example, we will return rows 10-20 on the salary column.

df_1.loc[10:20, 'salary']

	salary
10	56092
11	54452
12	54269
13	55125
14	97630
15	82444
16	76291
17	75382
19	62607
20	60373

You can return multiple columns using a list.

df_1.loc[:10, ['salary', 'publications']]

	salary	publications
0	86285	72
1	77125	43
2	71922	23
3	70499	64
4	66624	23
...	...	...
6	64366	22
7	59344	11
8	58560	8
9	58294	12
10	56092	4

11 rows × 2 columns

Indexing with Integers¶

Next we wil try .iloc. This method references the implicit python index using integer indexing (starting from 0, exclusive of last number). You can think of this like row by column indexing using integers.

For example, let's grab the first 3 rows and columns.

df_1.iloc[0:3, 0:3]

	salary	departm
0	86285	bio
1	77125	bio
2	71922	bio

Let's make a new data frame with just Males and another for just Females. Notice, how we added the .reset_index(drop=True) method? This is because assigning a new dataframe based on indexing another dataframe will retain the original index. We need to explicitly tell pandas to reset the index if we want it to start from zero.

male_df = df_1[df_1.gender == 0].reset_index(drop=True)
female_df = df_1[df_1.gender == 1].reset_index(drop=True)

Indexing with booleans¶

Boolean or logical indexing is useful if you need to sort the data based on some True or False value.

For instance, who are the people with salaries greater than 90K but lower than 100K ?

df_1[(df_1.salary > 90000) & (df_1.salary < 100000)]

	salary	departm	years	age	publications	pubperyear
14	97630	chem	34.0	64.0	43	1.264706
30	92951	neuro	11.0	41.0	20	1.818182
54	96936	physics	15.0	50.0	17	1.133333

This also works with the .loc method, which is what you need to do if you want to return specific columns

df_1.loc[(df_1.salary > 90000) & (df_1.salary < 100000), ['salary', 'gender']]

	salary	gender
14	97630	0
30	92951	0
54	96936	0

Numpy indexing¶

Finally, you can also return a numpy matrix from a pandas data frame by accessing the .values property. This returns a numpy array that can be indexed using numpy integer indexing and slicing.

As an example, let's grab the last 10 rows and the first 3 columns.

df_1.values[-10:, :3]

array([[53638, 0, 'math'],
       [59139, 1, 'bio'],
       [52968, 1, 'bio'],
       [55949, 1, 'chem'],
       [58893, 1, 'neuro'],
       [53662, 1, 'neuro'],
       [57185, 1, 'stat'],
       [52254, 1, 'stat'],
       [61885, 1, 'math'],
       [49542, 1, 'math']], dtype=object)

Renaming¶

Part of cleaning up the data is renaming with more sensible names. This is easy to do with Pandas.

Renaming Columns¶

We can rename columns with the .rename method by passing in a dictionary using the {'Old Name':'New Name'}. We either need to assigne the result to a new variable or add inplace=True.

df_1.rename({'departm': 'department', 'pubperyear': 'pub_per_year'}, axis=1, inplace=True)

Renaming Rows¶

Often we may want to change the coding scheme for a variable. For example, it is hard to remember what zeros and ones mean in the gender variable. We can make this easier by changing these with a dictionary {0:'male', 1:'female'} with the replace method. We can do this inplace=True or we can assign it to a new variable. As an example, we will assign this to a new variable to also retain the original lablels.

df_1['gender_name'] = df_1['gender'].replace({0: 'male', 1: 'female'})
df_1.head()

	salary	department	years	age	publications	pub_per_year	gender_name
0	86285	bio	26.0	64.0	72	2.769231	male
1	77125	bio	28.0	58.0	43	1.535714	male
2	71922	bio	10.0	38.0	23	2.300000	male
3	70499	bio	16.0	46.0	64	4.000000	male
4	66624	bio	11.0	41.0	23	2.090909	male

Operations¶

One of the really fun things about pandas once you get the hang of it is how easy it is to perform operations on the data. It is trivial to compute simple summaries of the data. We can also leverage the object-oriented nature of a pandas object, we can chain together multiple commands.

For example, let's grab the mean of a few columns.

df_1.loc[:, ['years', 'age', 'publications']].mean()

	0
years	14.972973
age	45.567568
publications	21.662162

We can also turn these values into a plot with the plot method, which we will cover in more detail in future tutorials.

# '%matplotlib inline' command supported automatically in marimo
df_1.loc[:, ['years', 'age', 'publications']].mean().plot(kind='bar')

Perhaps we want to see if there are any correlations in our dataset. We can do this with the .corr method. More recent versions of Pandas might produce an error if there are any columns containing string data. To avoid this issue set numeric_only=True.

df_1.corr(numeric_only=True)

	salary	gender	years	age	publications	pub_per_year
salary	1.000000	-0.300071	0.303275	0.275534	0.427426	-0.016988
gender	-0.300071	1.000000	-0.275468	-0.277098	-0.249410	0.024210
years	0.303275	-0.275468	1.000000	0.958181	0.323965	-0.541125
age	0.275534	-0.277098	0.958181	1.000000	0.328285	-0.498825
publications	0.427426	-0.249410	0.323965	0.328285	1.000000	0.399865
pub_per_year	-0.016988	0.024210	-0.541125	-0.498825	0.399865	1.000000

Merging Data¶

Another common data manipulation goal is to merge datasets. There are multiple ways to do this in pandas, we will cover concatenation, append, and merge.

Concatenation¶

Concatenation describes the process of stacking dataframes together. Older versions of pandas also had an .append() method, which has been deprecated since pandas 1.4. The main thing to consider is to make sure that the shapes of the two dataframes are the same as well as the index labels. For example, if we wanted to vertically stack two dataframe, they need to have the same column names.

Remember that we previously created two separate dataframes for males and females? Let's put them back together using the pd.concat method. Note how the index of this output retains the old index.

combined_data = pd.concat([female_df, male_df], axis = 0)

We can reset the index using the reset_index method.

pd.concat([male_df, female_df], axis = 0).reset_index(drop=True)

	salary	gender	departm	years	age	publications	pubperyear
0	86285	0	bio	26.0	64.0	72	2.769231
1	77125	0	bio	28.0	58.0	43	1.535714
2	71922	0	bio	10.0	38.0	23	2.300000
3	70499	0	bio	16.0	46.0	64	4.000000
4	66624	0	bio	11.0	41.0	23	2.090909
...	...	...	...	...	...	...	...
69	53662	1	neuro	1.0	31.0	3	3.000000
70	57185	1	stat	9.0	39.0	7	0.777778
71	52254	1	stat	2.0	32.0	9	4.500000
72	61885	1	math	23.0	60.0	9	0.391304
73	49542	1	math	3.0	33.0	5	1.666667

74 rows × 7 columns

We can also concatenate columns in addition to rows. Make sure that the number of rows are the same in each dataframe. For this example, we will just create two new data frames with a subset of the columns and then combine them again.

df1 = df_1[['salary', 'gender']]
df2 = df_1[['age', 'publications']]
df3 = pd.concat([df1, df2], axis=1)
df3.head()

	salary	age	publications
0	86285	64.0	72
1	77125	58.0	43
2	71922	38.0	23
3	70499	46.0	64
4	66624	41.0	23

Merge¶

The most powerful method of merging data is using the pd.merge method. This allows you to merge datasets of different shapes and sizes on specific variables that match. This is very common when you need to merge multiple sql tables together for example.

In this example, we are creating two separate data frames that have different states and columns and will merge on the State column.

First, we will only retain rows where there is a match across dataframes, using how=inner. This is equivalent to an 'and' join in sql.

df1_1 = pd.DataFrame({'State': ['California', 'Colorado', 'New Hampshire'], 'Capital': ['Sacremento', 'Denver', 'Concord']})
df2_1 = pd.DataFrame({'State': ['California', 'New Hampshire', 'New York'], 'Population': ['39512223', '1359711', '19453561']})
df3_1 = pd.merge(left=df1_1, right=df2_1, on='State', how='inner')
df3_1

	State	Capital	Population
0	California	Sacremento	39512223
1	New Hampshire	Concord	1359711

Notice how there are only two rows in the merged dataframe.

We can also be more inclusive and match on State column, but retain all rows. This is equivalent to an 'or' join.

df3_2 = pd.merge(left=df1_1, right=df2_1, on='State', how='outer')
df3_2

	State	Capital	Population
0	California	Sacremento	39512223
1	Colorado	Denver	NaN
2	New Hampshire	Concord	1359711
3	New York	NaN	19453561

This is a very handy way to merge data when you have lots of files with missing data. See Jake Vanderplas's tutorial for a more in depth overview.

Grouping

We've seen above that it is very easy to summarize data over columns using the builtin functions such as pd.mean(). Sometimes we are interested in summarizing data over different groups of rows. For example, what is the mean of participants in Condition A compared to Condition B? This is suprisingly easy to compute in pandas using the groupby operator, where we aggregate data using a specific operation over different labels. One useful way to conceptualize this is using the Split, Apply, Combine operation (similar to map-reduce).

This figure is taken from Jake Vanderplas's tutorial and highlights how input data can be split on some key and then an operation such as sum can be applied separately to each split. Finally, the results of the applied function for each key can be combined into a new data frame.

Groupby¶

In this example, we will use the groupby operator to split the data based on gender labels and separately calculate the mean for each group. Note that newer versions of pandas might throw an error if you try to perform a numeric computation such as .mean() on a dataframe containing columns of string data. Use the flag numeric_only=True to avoid this issue.

df_1.groupby('gender_name').mean(numeric_only=True)

	salary	gender	years	age	publications	pub_per_year
gender_name
female	55719.666667	1.0	8.666667	38.888889	11.555556	2.043170
male	69108.492308	0.0	15.846154	46.492308	23.061538	1.924709

Other default aggregation methods include .count(), .mean(), .median(), .min(), .max(), .std(), .var(), and .sum()

Transform¶

While the split, apply, combine operation that we just demonstrated is extremely usefuly to quickly summarize data based on a grouping key, the resulting data frame is compressed to one row per grouping label.

Sometimes, we would like to perform an operation over groups, but retain the original data shape. One common example is standardizing data within a subject or grouping variable. Normally, you might think to loop over subject ids and separately z-score or center a variable and then recombine the subject data using a vertical concatenation operation.

The transform method in pandas can make this much easier and faster!

Suppose we want to compute the standardized salary separately for each department. We can standardize using a z-score which requires subtracting the departmental mean from each professor's salary in that department, and then dividing it by the departmental standard deviation.

We can do this by using the groupby(key) method chained with the .transform(function) method. It will group the dataframe by the key column, perform the "function" transformation of the data and return data in same format. We can then assign the results to a new column in the dataframe.

df_1['salary_dept_z'] = (df_1['salary'] - df_1['salary'].groupby(df_1['department']).transform('mean')) / df_1['salary'].groupby(df_1['department']).transform('std')
df_1.head()

	salary	department	years	age	publications	pub_per_year	gender_name	salary_dept_z
0	86285	bio	26.0	64.0	72	2.769231	male	2.468065
1	77125	bio	28.0	58.0	43	1.535714	male	1.493198
2	71922	bio	10.0	38.0	23	2.300000	male	0.939461
3	70499	bio	16.0	46.0	64	4.000000	male	0.788016
4	66624	bio	11.0	41.0	23	2.090909	male	0.375613

This worked well, but is also pretty verbose. We can simplify the syntax a little bit more using a lambda function, where we can define the zscore function.

calc_zscore = lambda x: (x - x.mean()) / x.std()
df_1['salary_dept_z'] = df_1['salary'].groupby(df_1['department']).transform(calc_zscore)
df_1.head()

	salary	department	years	age	publications	pub_per_year	gender_name	salary_dept_z
0	86285	bio	26.0	64.0	72	2.769231	male	2.468065
1	77125	bio	28.0	58.0	43	1.535714	male	1.493198
2	71922	bio	10.0	38.0	23	2.300000	male	0.939461
3	70499	bio	16.0	46.0	64	4.000000	male	0.788016
4	66624	bio	11.0	41.0	23	2.090909	male	0.375613

For a more in depth overview of data aggregation and grouping, check out Jake Vanderplas's tutorial

Reshaping Data¶

The last topic we will cover in this tutorial is reshaping data. Data is often in the form of observations by features, in which there is a single row for each independent observation of data and a separate column for each feature of the data. This is commonly referred to as as the wide format. However, when running regression or plotting in libraries such as seaborn, we often want our data in the long format, in which each grouping variable is specified in a separate column.

In this section we cover how to go from wide to long using the melt operation and from long to wide using the pivot function.

Melt¶

To melt a dataframe into the long format, we need to specify which variables are the id_vars, which ones should be combined into a value_var, and finally, what we should label the column name for the value_var, and also for the var_name. We will call the values 'Ratings' and variables 'Condition'.

First, we need to create a dataset to play with.

data_7 = pd.DataFrame(np.vstack([np.arange(1, 6), np.random.random((3, 5))]).T, columns=['ID', 'A', 'B', 'C'])
data_7

	ID	A	B	C
0	1.0	0.987074	0.022615	0.391333
1	2.0	0.913296	0.442343	0.206280
2	3.0	0.364369	0.662187	0.146400
3	4.0	0.736500	0.632506	0.676784
4	5.0	0.139308	0.960752	0.155930

Now, let's melt the dataframe into the long format.

df_long = pd.melt(data_7, id_vars='ID', value_vars=['A', 'B', 'C'], var_name='Condition', value_name='Rating')
df_long

	ID	Condition	Rating
0	1.0	A	0.987074
1	2.0	A	0.913296
2	3.0	A	0.364369
3	4.0	A	0.736500
4	5.0	A	0.139308
...	...	...	...
10	1.0	C	0.391333
11	2.0	C	0.206280
12	3.0	C	0.146400
13	4.0	C	0.676784
14	5.0	C	0.155930

15 rows × 3 columns

Notice how the id variable is repeated for each condition?

Pivot¶

We can also go back to the wide data format from a long dataframe using pivot. We just need to specify the variable containing the labels which will become the columns and the values column that will be broken into separate columns.

df_wide = df_long.pivot(index='ID', columns='Condition', values='Rating')
df_wide

Condition	A	B	C
ID
1.0	0.987074	0.022615	0.391333
2.0	0.913296	0.442343	0.206280
3.0	0.364369	0.662187	0.146400
4.0	0.736500	0.632506	0.676784
5.0	0.139308	0.960752	0.155930

Exercises ( Homework)¶

The following exercises uses the dataset "salary_exercise.csv" adapted from material available here

These are the salary data used in Weisberg's book, consisting of observations on six variables for 52 tenure-track professors in a small college. The variables are:

sx = Sex, coded 1 for female and 0 for male
rk = Rank, coded
1 for assistant professor,
2 for associate professor, and
3 for full professor
yr = Number of years in current rank
dg = Highest degree, coded 1 if doctorate, 0 if masters
yd = Number of years since highest degree was earned
sl = Academic year salary, in dollars.

Reference: S. Weisberg (1985). Applied Linear Regression, Second Edition. New York: John Wiley and Sons. Page 194.

Exercise 1¶

Read the salary_exercise.csv into a dataframe, and change the column names to a more readable format such as sex, rank, yearsinrank, degree, yearssinceHD, and salary.

Clean the data by excluding rows with any missing value.

What are the overall mean, standard deviation, min, and maximum of professors' salary?

salary_file_url = 'https://raw.githubusercontent.com/ljchang/dartbrains/master/data/salary/salary_exercise.csv'

Exercise 2¶

Create two separate dataframes based on the type of degree. Now calculate the mean salary of the 5 oldest professors of each degree type.

Exercise 3¶

What is the correlation between the standardized salary across all ranks and the standardized salary within ranks?