# Pandas Tutorial

In pandas we have two datastructure:

- Series, which can be thought as an array or as pandas 1D data structure)
- DataFrame, which can be thought as a matrix or as pandas 2D data structure)

A DataFrame can be also viewed as an array of Series, dataframes and series are flexible, and can contain labels for fields, row indexes and many other interesting features which would be complicated to have on plain arrays/matrices.

### Creating a Dataframe

df1 = pd.DataFrame({ "city": ["new york","chicago","orlando"], "temperature": [21,14,35], })

### Describe a Dataset

ds.describe(include = "all") ds.info() ds.memory_user(deep = True)

### Print a Dataset

Generally pandas will print only a subset of all the rows, in order to keep the
screen clean and not have a huge output, anyway sometimes, we need to really
print the entire dataframe on screen, in these cases we can simply use the
`to_string()`

method accompanied by a `print()`

call.

print(ds.to_string())

### Types

We can view and inspect types of a dataframe with:

ds.dtypes

In order to change a column to a categoric type we can do:

ds['column_name'] = ds['column_name'].astype('category', categories=['good', 'very good', 'excellent'])

### Basic Pandas Statistics

ds.field_name.mean()

ds.field_name.median()

ds.field.quantile([0.1,0.15, .9])

We can plot more percentiles with the commodity of list comprehensions, the following will plot all the percentiles:

quantiles_lst = [x * 0.01 for x in range(0, 101)] ds.field.quantile(quantiles_lst)

The following will print the deciles like: 0.1, 0.2, 0.3, ...

quantiles_lst = [x * 0.1 for x in range(0, 11)] ds.field.quantile(quantiles_lst)

The following will print the percentiles multiples of 5 like: 0.05, 0.1, 0.15, 0.2, ...

quantiles_lst = [x * 0.01 for x in range(0, 101) if x % 5 == 0] ds.field.quantile(quantiles_lst)

### Manipulating CSV Files

#### Reading a CSV File

We can read a csv file in this way:

ds = pd.read_csv(filename, sep=None, engine='python', parse_dates=['fcast_date','timestamp'], dtype={'user_id': "category", 'stringa':'object'})

Basically we set engine to python anytime we deal with regexes.

Let's see another example:

# In this case we are also setting an index column ds = pd.read_csv("reuters_random_sample.csv", parse_dates=['time', 'published_time'], index_col='time')

let's see another example:

# in this case we skip the initial space we have in fields, this is very useful # since many times we have csv files where fields are separated by a space other # than commas to increase readability ds = pd.read_csv("reuters_random_sample.csv", parse_dates=['time', 'published_time'], index_col='time', skipinitialspace = True)

Let's see another example where we want to exclude some columns or change the order of the existing columns:

# in this case we read the cols but then switch the order in our dataframe ds = pd.read_csv(data, usecols=['foo', 'bar'], skipinitialspace=True)[['bar', 'foo']]

we can also refer to columns numerically, for example:

ds = pd.read_csv(data, usecols=[0,1], comment='#')

In this last case we also specified that lines starting with "#" have to be considered comments, hence not to be analyzed.

Let's see another example, in this case we have fields separated by a bunch of spaces, but still spaces can appear in some of the fields because there are strings, for example:

1 "a string exampel" 12:32 "awdaw ddwd wa da " 2 "a string exampel, dwao9*(0323" 12:35 "a a awdaw ddwd, wa,, da "

In this case we can read the file, by denoting the quoting char, so inside quoting chars the separator can apper and will not cause any problems

ds = pd.read_csv("data.csv", sep='\s+', engine='python', quotechar='"')

Another example could be when we have multiple separators, at this point we can try with:

# In this case we consider both ; and , as separators df = pd.read_csv("file.csv", sep="[;,]", engine='python')

ds = pd.read_csv("original_datasets/newaa", engine='python', quotechar='!', header=None, names=['time','offset','title','link'], index_col='time')

##### Reading an XLS(X) file

energy = pd.read_excel("Energy Indicators.xls")

#### Writing to a CSV File

Let's see how to save our dataframe to a new csv file:

# In this case we do not want to save the index to the file ds.to_csv(filename, index = False)

### Selecting Data

In pandas we generally select data through the use of two methods:

- loc, which selects by using labels
- iloc, which selects by using integer numbers
- ix, which selects using an index

We may also avoid the usage of these methods and let pandas infer if we are selecting by label or by an index integer number, but this is not adviced, it is always better to be specific to not make the code look ambiguous.

#### Selecting with Labels (i.e., loc)

In order to select by labels we use the loc method:

ds.loc[0:4, ['column1','column2']]

This can be considered another way to remove columns and just keep those in which we are interested:

ds.loc[:, ['column1','column2']]

ds.loc[0:4, 'column1':'column2']

df.loc[:, df.columns.str.startswith('foo')]

If we have indexes which are not integer, we can take advantage of loc capabilities, e.g.:

df.loc['2016-01-11', ['column1', 'column2']]

#### Selecting and changing a specific value

If we want to modify the value in column 'b' which is on the first row we can do:

df.loc[1, 'b'] = 'XXXXXX'

#### Selecting with Numbers (i.e., iloc)

We can use iloc if we want to select data referring to numbers for columns like:

ds.iloc[:, 0:4]

We can also combine iloc and loc with:

army.loc['Arizona'].iloc[2]

This will select the row with the index name called 'Arizona' and the third column belonging to this raw

#### Selecting with Indexes(i.e., ix) (this is deprecated)

Let's say we want to print the row with the maximum value for a specific column, we can do:

max_index = df.columnname.idxmax() df.ix[max_index]

### Filters

ds[(ds.column1 >= 200) & (ds.column2 == 'Drama')]

### Pandas Conditionals

df.loc[df.AAA >= 5,['BBB','CCC']] = 555;

df['logic'] = np.where(df['AAA'] > 5,'high','low'); df

### Column Operations

#### Remove columns

ds.drop(['column1','column2'], 1, inplace = True)

or easier with:

del crime['column1']

#### Remove Column on a Condition

c = c[c.n_opts != 5]

#### Rename columns

ds.rename(columns={'fcast_date_a': 'date_fcast'}, inplace=True)

#### Create new Columns

ds["days_from_start"] = ds["fcast_date_a"] - ds["date_start"]

#### Create new Columns with Apply

def compute_euclidean_distance(row): a = np.array([row['value_a'], row['value_b'], row['value_c']]) b = np.array([row['a'], row['b'], row['c']]) return distance.euclidean(a, b) ds['new_distance'] = ds.apply(compute_euclidean_distance, axis=1)

#### Create new Columns based on difference of Rows

We can use the shift function to create a new dataset which is shifted by one position, for example, in the case where our dataset represents HTTP requests arriving at a webserver, we can compute the interarrival column by just doing:

ds['diff'] = ds['time_in_sec'] - ds['time_in_sec'].shift(1)

of course the first element will be a NaN, which we have to deal with, since it has no corresponding element to perform the subtraction in this case.

Another common usage of the shift function is when we want to create a dataset which can be used with an AR model or in general with time series.

This can be done for example like this:

def create_sequence_ds(ds, colname_to_shift, num_steps_backward): for i in range(num_steps_backward): ds['shift_'+str(i)] = ds[colname_to_shift].shift(i+1) return ds

#### Inspect Column Values

How many items for each category in a column?

df.column_name.value_counts()

How many different items for a specific category in a column?

df.column_name.value_counts().count()

or faster with:

df.column_name.nunique()

Remember that *value_counts* is useful for ordering a categorical variable
while *sort_values* is useful when ordering a numerical variable or a categorical
variable for which an order is specified.

Let's see a couple of examples:

df.column_name_cat.value_counts(ascending = False)

while for a numerical variable we can do:

df.column_name_cat.sort_values(ascending = False)

#### Create Dummy Columns for One-Hot Encoding

one_hot_cols = pd.get_dummies(ds['outcome'], prefix='outcome') ds.drop('outcome', axis=1, inplace = True) ds = ds.join(one_hot_cols)

#### Create Dummy Columns for Dummy Encoding

one_hot_cols = pd.get_dummies(ds['outcome'], prefix='outcome', drop_first=True) ds.drop('outcome', axis=1, inplace = True) ds = ds.join(one_hot_cols)

#### Create a categorical variable from a continuous variable

We can create ranges for continuous variable to transform them into categorical variables, in pandas we can do this with:

ds['RenewCat'] = pd.cut(ds['% Renewable'], bins=5)

In this case we are using 5 bins, of course we can use more bins and have more categories.

If we precisely know the interval values to which we want to perform the split we can do:

ds['newcol'] = pd.cut(ds['age'], bins=[0, 18, 35, 70])

notice that the intervals are inclusive, so the first one will go from 0 to 18 included, while the second one will go from 18 excluded to 35 included and so on.

Other ways to discretize features are using numpy with:

discretized_age = np.digitize(age, bins=[20,30,64])

In this last case if we have a series called age which is made like this:
`6, 12, 20, 36, 65`

, after the operation, digitized_age will be like this:
`0, 0, 1, 1, 1`

.

So the bin numbers are exclusive.

#### Create a Dataframe as a combination of two dataframes with different columns

The main purpose of a cross-tabulation is to enable readers to readily compare two categorical variables.

ds = pd.concat([df_even, df_odd], axis=1)

### Row Operations

ds["ifp_id"].unique()

#### Deleteing Rows which have missing values

df.dropna()

#### Split a Dataset into Train/Test

train = dataset.sample(frac=0.95,random_state=200) test = dataset.drop(train.index)

#### Select Rows based on Condition

ds[ds['colname1'] == 'value'] # Let's select all the rows which have as value # in colnam2 the string America or Europe ds[ds['colnam2'].isin(['America','Europe'])] # Now we perform a negation of the previous filter with # the character '~' ds[~ds['colnam2'].isin(['America','Europe'])]

#### Concatenate rows of two different datasets with same columns

In order to concatenate rows of more datasets we can basically do:

pd.concat([df1, df2, df3], ignore_index = True)

A useful shortcut to concat() are the append() instance methods on Series and DataFrame. These methods actually predated concat. They concatenate along axis=0, namely the index:

result = df1.append(df2, ignore_index = True)

Another example of this, is when our dataset is split among more files, in this case we can do:

frames = [ process_your_file(f) for f in files ] result = pd.concat(frames, ignore_index = True)

### Merge

We can combine dataset by using merge, as in database theory we must understand what it means to do:

- Outer Join (Union), we take everything, this is the equivalent of a union
- Inner Join (Intersection), we take only things which are in both sets (i.e., dataframes) this is the equivalent of an intersection
- Conditional Joins (Left and Right Joins), this is the equivalent of an intersection with a union with one of the sets, or dataframes

By default pandas performs inner joins.

#### Outer Join

pd.merge(df1, df2, how = 'outer', left_index = True, right_index = True)

#### Inner Join

pd.merge(df1, df2, how = 'inner', left_index = True, right_index = True)

In order to merge on a field which could be considered a primary key we can do:

c = pd.merge(ds1, ds2, on='ifp_id')

or

print(pd.merge(products, invoices, left_index=True, right_on='Product ID'))

Now this is by default an inner join, that means, that only the 'ifp_id' which are intersection of both ds1 and ds2 are taken into account.

We can do an outer join by specifying the attribute called 'how'.

df3 = pd.merge(df1,df2,on="city",how="outer")

We can also specify if we want to keep all the keys containes only in the left dataset or right dataset with:

df3 = pd.merge(df1,df2,on="city",how="left")

If we have column names which are shared by both datasets we can easily add suffixes, for example:

df3 = pd.merge(df1,df2,on="city",how="outer", suffixes=('_first','_second'))

or let's say we have a couple of predictions, so user in table 1 has value1 value2 value3 and also a user in table 2, so we can do:

df3 = pd.merge(df1,df2,on="ifp_id",how="inner", suffixes=('_user1','_user2'))

#### Removing Rows

We can remove rows, for example in order to remove header and footer information from a dataset we can do:

ds = ds[8:246]

this will take from the 8th row to the 245th row of the dataset.

#### Conditional Joins (Left and Right)

Let's say that df1 is a dataset related to the staff of a university while df2 is the dataframe related to the students.

We can create a new dataframe containing all the staff and information about students only if the staff members are students with a left join:

pd.merge(df1, df2, how = 'left', left_index = True, right_index = True)

In the other case, if we want to have all students but include information for the ones who are of the staff (who is not belonging to the staff will have these info at Null we can do:

pd.merge(df1, df2, how = 'right', left_index = True, right_index = True)

### Removing Data from Datasets

In order to remove all rows where the field 'Quantity' is equal to 0 we can do:

df.drop(df[df['Quantity'] == 0].index)

### Dealing with Null Values

#### Summarizing Null Values

ds.isna().sum()

#### Removing Null Values

In order to drop all the rows which have a null value on *any* field we do:

ds.dropna(how='any')

In order to drop all the rows which have a null value on *all* the field we do:

ds.dropna(how='all')

In order to drop all the rows which have a null value on *any* field within a subset we do:

ds.dropna(subset = ['column', 'column2'], how='any')

In order to drop all the rows which have a null value on *all* the fields within a subset we do:

ds.dropna(subset = ['column', 'column2'], how='all')

#### Reaplacing Null Values

ds['column_name'].fillna(value='not assigned', inplace = True)

ds['columnname'].value_counts(dropna = False)

### Dealing with Duplicates

#### Counting Duplicates

In order to count all the duplicated values we do:

ds.duplicated().sum()

In order to count all the duplicated values with respect to a certain subset of fields we do:

ds.duplicated(subset['age','zip_code']).sum()

In order to count duplicates with respect to a certain column we can do:

ds['column_name'].duplicated().sum()

#### Visualizing Duplicates

In order to view all the duplicates we can do:

ds.loc[users.duplicated(keep = 'last'), :]

where keep = 'last' means that we are showing the last encountered instance of a duplicate row

#### Removing Duplicates

To remove duplicates and just keep the first encountered instances we do:

ds.drop_duplicates(keep = 'first')

To remove duplicates and just keep the last encountered instances we do:

ds.drop_duplicates(keep = 'last')

To remove duplicates with respect to a subset of fields:

ds.drop_duplicates(subset = ['age', 'zip_code'])

### Sorting Values

We can sort by a specific column by doing:

ds.sort_values(['column_1'], ascending=False)

We can also sort using multiple columns by doing:

dfworking = dfworking.sort_values(['STATE','DISTRICT','GENERAL VOTE'], ascending=[True, True, False])

We can also take the top values for a specific dataframe with *nlargest*:

df.nlargest(3, 'column_name')

Or the lowest values with:

df.nsmallest(3, 'column_name')

Another way to sort a dataset by the value of a column inplace is:

df.sort('rank',inplace=True)

### Comparing Values

We can check if two series or dataframe are equal, i.e., they have the same values with:

assert ds['columnname'].equals(ds2['anothercolumn'])

Another example using dataframes instead of series may be:

ds.equals(ds2)

### Grouping Values

Let's say we have a dataset which tells us various characteristics for neighbourhoods for different cities, let's say now we want to understand things related to cities, a way to do this is to use groupby. By using groupby we can imagine, that we are splitting our dataframe in multiple smaller dataframes, each related to a specific thing. For example taking our city and neighbourhoods dataframe, a groupby on the column 'city', would produce different dataframes, each one containing only rows related to a specific city.

#### Printing a groupby dataframe

g = df.groupby('city') for city, city_df in g: print(city) print(city_df)

An alternative way could be:

g.get_group("Napoli")

With the last command meaning "print only the sub dataframe related to the city Napoli".

A groupby basically implements a sequence of the following operations:

- split, splits the original DataFrame in sub DataFrames
- apply, applies an operation, we can ask for max(), min(), mean() and whatever other function
- combine, again in a new Series or DataFrame

ds.groupby('column_name').column2.mean()

ds.groupby('column_name').column2.max()

ds.groupby('continent').mean()

ds.groupby('city').describe()

We can also apply these statistics for each row, for example let's say that we have a dataset and many columns represent years, while the values are some energy spent through these years, we can compute the average or max, min by using the axis = 1 parameter, e.g.:

years = ['2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015'] ds['avg_energy'] = Top15[years].mean(axis = 1)

We can also plot data for each different city for example:

ds.groupbby('city').plot()

We can also use multiple columns in the groupby for example, let's say that we want to print the mean age for each combination of occupation and gender, we can do:

users.groupby(['occupation','gender']).mean().age

### Map, Apply and ApplyMap

Map, Apply and ApplyMap operations are used to perform transformations on a dataset.

Map applies a translation to each element of a series:

ds['new_column'] = ds.column.name.map({'female':0, 'male':1})

an alternative to map is to insert all the substitutions in a dictionary and then use replace, like this:

subs = {"Republic of Korea": "South Korea", "United States of America": "United States", "United Kingdom of Great Britain and Northern Ireland": "United Kingdom", "China, Hong Kong Special Administrative Region": "Hong Kong"} ds.replace({'column_name":subs}, inplace = True)

Apply applies a function to each element of a series

ds['new_column'] = train.col1.apply(len)

let's see another example:

energy['Country'] = energy['Country'].apply(remove_digit)

We use *apply* everytime we want to build new columns based for example
on values of the rows, for example, let's say we want to build a new column
which keeps track of the average of the fields dist1, dist2 and dist3
we can do:

def avg_dist(row): row['avg_dist'] = (row['dist1'] + row['dist2'] + row['dist3']) / 3 return row df.apply(avg_dist, axis = 1)

Let's see another example where we add a column called *legal_drinker* which
says if a person in a dataset can drink or not in the US:

def majority(row): if row > 17: return True else: return False df['legal_drinker'] = df.age.apply(majority)

an alternative way to implement it is:

def majority(row): if row['age'] > 17: row['legal_drinker'] == True else: row['legal_drinker'] == False return row df.apply(majority, axis =1)

so keep in mind that if we consider the fields of the rows specifically, we have
to add the field *axis = 1* to apply.

Another example, could be if we want to find the maximum and minimum among a set of columns, in this case we can do:

def min_max(row): data = [['POPESTIMATE2010', 'POPESTIMATE2011', 'POPESTIMATE2012', 'POPESTIMATE2013', 'POPESTIMATE2014', 'POPESTIMATE2015']] row['max'] = np.max(data) row['min'] = np.min(data) return row df.apply(min_max, axis = 1)

Note: The most commonly used method is map, while applymap is more rarely used.

Let's see another example where we use a lambda expression, or anonymous function:

# Here we change an integer column to a timedelta in hours. ds['time_offset_col'] = ds.time_offset_col.apply(lambda x: pd.Timedelta(hours=x))

Let's see another example where we want to pass to the function called in apply more arguments, this can be done using lambdas like this:

def apply_labeling(x, time, other_time): if (x['intertime_s'] >= time and x['intertime_s2'] >= other_time): return 300 else: return 100 ds['new_label'] = ds.apply(lambda x : apply_labeling(x, 300, 500), axis=1)

#### Operations to perform on Groups

On dataframes where we used groupby we can generally perform different operations, let's see some examples:

- if we want to get a single value for each group - use
`aggregate()`

- if we want to get a subset of the input rows - use
`filter()`

- if we want to get a new value for each input row - use
`transform()`

### Cross Tab

The main purpose of a cross-tabulation is to enable readers to readily compare two categorical variables:

pd.crosstab(ds.column_x, ds.column_y)

### Plotting with Pandas

Let's see some plotting which is generally done with pandas, when I have to do plots I prefer to generally do:

import pandas as pd import matplotlib.pyplot as plt

#### Line Plots

If we have a dataframe in which we can plot more columns as lines we can do:

a.plot(x = 'col1', y = ['col2','col3'])

This will plot automatically a figure with a legend and on the x axis we will have the values belonging to col1 while on y axis with different colors we will have the values of col2 and col3.

If we do not specify the parameter 'x', matplotlib will automatically use the dataframe index as 'x'.

By default the plot() function uses as parameter 'kind' the value 'line', so automatically plots a line plot.

#### Scatter Plots

We can make a scatter plot of two columns of a dataframe like this:

df.plot(kind='scatter', x='Height', y='Weight')

Now let's say we want to plot more things on the same plot, what we can do is use the parameter 'ax' to refer to the same plot.

For example:

fig, ax = plt.subplots() males.plot(kind='scatter', x='Height', y='Weight', ax=ax, color='blue', alpha=0.3, title='Male & Female Populations') females.plot(kind='scatter', x='Height', y='Weight', ax=ax, color='red', alpha=0.3)

Or another thing we can do is to add to our dataframe a color column and then add the 'c' parameter:

df['Gendercolor'] = df['Gender'].map({'Male': 'blue', 'Female': 'red'}) df.plot(kind='scatter', x='Height', y='Weight', c=df['Gendercolor'], alpha=0.3, title='Male & Female Populations')

We can also specify the value range on the axis with the parameters 'xlim' and 'ylim', like this:

df.plot(kind='scatter', x='col1', y='col2', xlim=(-1.5, 1.5), ylim=(0, 3))

#### Histogram Plots

We can plot histograms like this:

df['Height'].plot(kind='hist', bins=50, alpha=0.3, color='blue')

we can also specify a range by doing:

df['Height'].plot(kind='hist', bins=50, alpha=0.3, range = (30,100), color='blue')

We can also have the mean or median line overimposed on an histogram plot, for example by doing:

plt.axvline(males['Height'].mean(), color='blue', linewidth=2) plt.axvline(females['Height'].mean(), color='red', linewidth=2)

##### Plotting the Cumulative Distribution

We can plot the cumulative distribution of a column, like this:

df.column1.plot(kind='hist', bins=100, title='Cumulative distributions', normed=True, cumulative=True, alpha=0.4)

#### Plotting an estimate of the Probability Density Function

In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzenâ€“Rosenblatt window method.

df.col1.plot(kind='kde')

#### Box Plots

df.column1.plot(kind='box', color = 'red', title='Boxplot')

We can also plot boxplots horizontally like this:

df.plot.box(vert=False, positions=[1, 4, 5, 6, 8]) # here we also specified the positions

color = dict(boxes='DarkGreen', whiskers='DarkOrange', medians='DarkBlue', caps='Gray') df.plot.box(color=color, sym='r+')

#### Bar Plots

ds.column_name.plot(kind = 'bar')

#### Combination of more plots

fig, ax = plt.subplots(2, 2, figsize=(5, 5)) df.plot(ax=ax[0][0], title='Line plot') df.plot(ax=ax[0][1], style='o', title='Scatter plot') df.plot(ax=ax[1][0], kind='hist', bins=50, title='Histogram') df.plot(ax=ax[1][1], kind='box', title='Boxplot') plt.tight_layout() # this is used in order to not have titles imposed on plots

#### Scatter Matrix Plots

We can also plot scatter plots for all the features:

from pandas.plotting import scatter_matrix scatter_matrix(df, alpha=0.2, figsize=(10, 10), diagonal='kde')

This not only allows us to have a lot of plots, but puts on the diagonal the probability density function estimation with the KDE method, we can change this by putting 'hist'.

#### Pie Plots

gt01 = df['data1'] > 0.1 piecounts = gt01.value_counts() # Piecounts will have only two values with a specific count piecounts.plot(kind='pie', figsize=(5, 5), explode=[0, 0.15], labels=['<= 0.1', '> 0.1'], autopct='%1.1f%%', shadow=True, startangle=90, fontsize=16)

#### Hexbin Plots

df.plot(kind='hexbin', x='x', y='y', bins=100, cmap='rainbow')

#### Correlation Plots

In order to view a correlation plot we can do:

import matplotlib.pyplot as plt plt.matshow(df.corr())

#### Parallel Coordinates Plot

Parallel coordinates is a plotting technique for plotting multivariate data, see the Wikipedia entry for an introduction. Using parallel coordinates points are represented as connected line segments. Each vertical line represents one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together.

from pandas.plotting import parallel_coordinates plt.figure() parallel_coordinates(df, 'Title')

The PCA and LDA plots are useful for finding obvious cluster in the data, in the other side scatter plot matrices or parallel coordinate plots show specific behavior of features in a dataset.

#### Lag Plots

Lag plots are used to check if a data set or time series is random. Random data should not exhibit any structure in the lag plot. Non-random structure implies that the underlying data are not random.

lag_plot(data)

#### Autocorrelation Plots

Autocorrelation plots are often used for checking randomness in time series. This is done by computing autocorrelations for data values at varying time lags.

Autocorrelation plots are often used for checking randomness in time series. This is done by computing autocorrelations for data values at varying time lags. If time series is random, such autocorrelations should be near zero for any and all time-lag separations. If time series is non-random then one or more of the autocorrelations will be significantly non-zero. The horizontal lines displayed in the plot correspond to 95% and 99% confidence bands. The dashed line is 99% confidence band.

#### Decorating Plots

We can add lines to indicate points or regions with:

# draws a vertical line plt.axvline(0.2, color='r') # draws an horizontal line plt.axhline(0.5, color='b')

#### Visualizing Unstructured Data

In order to visualize unstructured data (e.g., audio, immages, text, ...), we can make use of common packages generally used along with pandas.

##### Audio

For the audio, we can see the signal with:

from scipy.io import wavfile rate, snd = wavfile.read(filename = 'nameoffile.wav') plt.plot(snd)

We can also view the spectrogram by doing:

_ = plt.specgram(snd, NFFT=1024, Fs=44100) plt.ylabel('Frequency (Hz)') plt.xlabel('Time (s)')

##### Images

We can visualize images with:

from PIL import Image import numpy as np img = Image.open('../path/name.jpg') imgarray = np.asarray(img) # This gives us an array imgarray.shape # with this we can understand the shape

At this point we could use ravel() or reshape() to change the size as we wish.

#### Setting Plot Options

Once we have a plot with pandas:

hist_plot = ds.colnam1.plot(kind='hist', bins=50) hist_plot.set_xlim(-200,200) hist_plot.set_xlim(-350,350)

Another parameter used when plotting is the label, notice that labels support latex, so we can do:

ax.plot(x, i * x, label='$y = %ix$'.format(i))

Or

bar_plot = ds.colnam1.plot(kind='hist', bins=50) bar_plot.set_xlabel("x label") bar_plot.set_ylabel("y label")

#### Other Plotting Utilities

We can instantiate a new plot with a title by doing:

import matplotlib.pyplot as plt plt.figure("title of the figure") # This states, create a plot with 3 figures, and position # them vertically # the general structure is subplot(nrows, ncols, index) # here we will position the figure in the structure 3,1 # at index 1 plt.subplot(311) # To set a scale on y axis we can use plt.ylim([0,350]) ds0.plot() # here we will position the figure in the structure 3,2 # at index 2 # To set a scale on y axis we can use plt.ylim([0,350]) plt.subplot(312) ds1.plot() # here we will position the figure in the structure 3,3 # at index 3 # To set a scale on y axis we can use plt.ylim([0,350]) plt.subplot(313) ds2.plot()

We can also choose a stylesheet, for example we can have the same style of the infamous ggplot package in R with:

import matplotlib.pyplot as plt plt.style.use('ggplot')

### Correlation with Pandas

We can compute the pearson correlation index between two columns with:

Top15['column1'].corr(Top15['column2'])

By default pandas compute the Pearson correlation, but we can compute other kinds of correlation indexes by specifying other options, such as:

Top15['column1'].corr(method='spearman', Top15['column2']) Top15['column1'].corr(method='kendall', Top15['column2']) # This happens by default Top15['column1'].corr(method='pearson', Top15['column2'])

We can show the correlation matrix using Pearson's Correlation Index with:

import matplotlib.pyplot as plt plt.matshow(dataframe.corr())

### Time Series Analysis with Pandas

A time series is a set of data points indexed in time oder, for example stock prices during a year, or a specific physical value in time.

In order to parse date correctly we can specify our own customm function to deal with dates:

dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m') data = pd.read_csv('AirPassengers.csv', parse_dates=['Month'], index_col='Month',date_parser=dateparse)

Let's say for example that our dates in a file are surrounded by square brackets as in apache web server logs, at this point we could also strip those characters.

We can also defer the parsing and setting of a time/date field by doing:

data['time'] = pd.to_datetime(data['time'], format = "%Y%m%d %I:%M %p") # sometimes the format can be auto inferred by pandas # data['time'] = pd.to_datetime(data['time']) data.set_index('time', inplace=True)

#### Time Series Aggregation

Let's say we want to aggregate our time series by hour, or by minute, or by day,
we can do it using the `resample`

method.

Let's say we just have a bunch of timestamps, which do not have a specific structure, like by minute, or by hour and so on. For example they could represent the access times made to a web page, so we basically have timestamps without any other information.

Like this:

2007-05-05 18:51:37 2007-05-05 18:54:02 2007-05-05 19:59:11 2007-05-05 19:59:11 2007-05-05 19:59:11 2007-05-06 22:33:18 2007-10-26 08:17:42

We can transform this data to a timeseries with a specific resolution in this way:

# we first set a 1 to each timestamp, which is useful for the aggregation # into a time series ds['count'] = 1 # these are some examples of possible aggregations ds_minute = ds.resample('T').sum() # minute ds_15minute = ds.resample('15T').sum() # 15 minutes ds_hour = ds.resample('H').sum() # hour ds_day = ds.resample('D').sum() # day ds_week = ds.resample('W').sum() # week ds_month = ds.resample('M').sum() # month ds_year = ds.resample('A').sum() # year

#### Time Series Common Tasks: Converting Date Format

##### From Unix Time to Human Readable Date

df['date'] = pd.to_datetime(df['date'],unit='s')

In order to convert to Unix Time a Human Readable date, we can do:

ds['time'] = (ds['time'].astype(np.int64)/1e9).astype(np.int64)

#### Time Series Common Tasks: Getting the Day of the Week

Sometimes it can be useful to get the weekdays to be able to divide our dataset into working week days and weekend days. This can be easily achieved with:

series['day_of_week'] = series.index.weekday_name ds_week = series[~series['day_of_week'].isin(['Saturday','Sunday'])] ds_weekend = series[series['day_of_week'].isin(['Saturday','Sunday'])] # Now we can remove the fields of the name if we don't need them del series['day_of_week'] del ds_week['day_of_week'] del ds_weekend['day_of_week']

#### Time Series Common Tasks: Filtering a time series with dates

We can filter a time seris in this way:

date_mask = (ds.index >= "2010-05-01") & (ds.index < "2010-07-01") ds[date_mask]

Now we are saying take all the days starting from the first of may, (this is included) until the last day of june.

I think there is no difference in terms of dates between > and >=.

We can filter data by dates like in multiple ways, let's see another example:

# We pick all the data points from the beginning of 2015 to the end of 2016 date_mask = (ds_utc.index >= "2015-01-01") & (ds_utc.index < "2017-01-01") # Now we take from 2012 to 2014 ds_utc_2y = ds_utc[date_mask]

#### Time Series Common Tasks: Converting time in different units

If we want to have the difference in hours between to pandas datetimes we can do:

ds['difference_in_hours'] = (ds['published_time'] - ds.index).astype('timedelta64[h]')

If we have a timedelta and just want to convert it into an integer number of seconds, we can do:

df['duration_seconds'] = df['duration'] / np.timedelta64(1, 's')

### Appendix A: Pandas Options

Change the maximum number of printable rows:

pd.set_option('display.height', 500) pd.set_option('display.max_rows', 500)

### Appendix B: Other Tricks

#### Getting the maximum among more columns

To create an additional column which is the maximum among different columns we can simply do:

dss['top_topic_value'] = dss[['topic_0','topic_1','topic_2']].max(axis=1)

Anyway what if we need to get the column name which has the maximum value?
In this case we can simply use the `idxmax`

method, like this:

dss['top_topic'] = dss[['topic_0','topic_1','topic_2']].idxmax(axis=1)

#### Cumulative Sum of a Column

Given a column, we can build a cumulative sum of the column by using:

ds['cum_sum'] = ds.columnname.cumsum() ds['cum_perc'] = 100*ds.cum_sum/ds.columnname.sum() ds.cum_perc.plot() # plots the cumulative distribution