from jupyterthemes import jtplot
import warnings
from imblearn.over_sampling import SMOTE
import seaborn as sns
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import pandas_profiling
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing
import matplotlib.pyplot as plt
%matplotlib inline
# ignore warnings
warnings.filterwarnings('ignore')

jtplot.style(theme='oceans16', context='notebook',
             ticks=True, grid=False, figsize=(10, 9))

df=pd.read_csv('../processed_data/nf_complete.csv')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 126 entries, 0 to 125
Data columns (total 23 columns):
 #   Column                 Non-Null Count  Dtype 
---  ------                 --------------  ----- 
 0   Unnamed: 0             126 non-null    int64 
 1   year                   126 non-null    int64 
 2   title                  126 non-null    object
 3   abstract               126 non-null    object
 4   theme                  126 non-null    object
 5   China                  126 non-null    int64 
 6   Russia                 126 non-null    int64 
 7   War                    126 non-null    int64 
 8   President              126 non-null    int64 
 9   US                     126 non-null    int64 
 10  Vietnam                126 non-null    int64 
 11  Cold War               126 non-null    int64 
 12  World War              126 non-null    int64 
 13  Vietnam War            126 non-null    int64 
 14  Korean War             126 non-null    int64 
 15  Survey                 126 non-null    int64 
 16  Case Study             126 non-null    int64 
 17  Trade                  126 non-null    int64 
 18  Humanitarian           126 non-null    int64 
 19  fixed_effects          126 non-null    int64 
 20  instrumental_variable  126 non-null    int64 
 21  regression             126 non-null    int64 
 22  experimental           126 non-null    int64 
dtypes: int64(20), object(3)
memory usage: 22.8+ KB

import plotly_express as ple
ple.histogram(df.sort_values('year').groupby(['year','theme'])['Cold War'].sum().reset_index(), x="year", y="Cold War", histfunc="sum", color="theme")

# ple.lidifferences(dfm_regional.sort_values('year').groupby(['year','theme'])['Cold War'].sum().reset_index(),
#          x='year',
#          y='Cold War',
#          line_group='theme',
#          color='theme'
#         )

# Create the crosstab DataFrame
pd_crosstab = pd.crosstab(df["theme"], df["year"])

# Plot a heatmap of the table with no color bar and using the BuGn palette
sns.heatmap(pd_crosstab, cbar=False, cmap="GnBu", linewidths=0.3)

# Rotate tick marks for visibility
plt.yticks(rotation=0)
plt.xticks(rotation=90)

plt.tight_layout()
#plt.savefig('./img/theme_heat_1.png', bbox_inches='tight', dpi=500)


#Show the plot
plt.show()
plt.clf()

<Figure size 720x648 with 0 Axes>
sns.clustermap(pd_crosstab, cmap='Greens', robust=True)

# plot using a color palette
#sns.heatmap(df, cmap="YlGnBu")
#sns.heatmap(df, cmap="Blues")
#sns.heatmap(df, cmap="BuPu")
#sns.heatmap(df, cmap="Greens")

<seaborn.matrix.ClusterGrid at 0x7f978c07a470>
# Import seaborn library
import seaborn as sns

# Get correlation matrix of the meat DataFrame
corr_meat = df.corr(method='pearson')

# Customize the heatmap of the corr_meat correlation matrix and rotate the x-axis labels
fig = sns.clustermap(pd_crosstab,
                     row_cluster=True,
                     col_cluster=True,
                     figsize=(10, 10))

plt.setp(fig.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
plt.setp(fig.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
plt.show()

data_normalized = pd_crosstab

# Standardize the mean and variance within a stat, so different stats can be comparable
# (This is the same as changing all the columns to Z-scores)
data_normalized = (data_normalized - data_normalized.mean())/data_normalized.var()

# Normalize these values to range from -1 to 1
data_normalized = (data_normalized)/(data_normalized.max() - data_normalized.min())

data_normalized = data_normalized.T

# Can use a semicolon after the command to suppress output of the row_dendrogram and col_dendrogram.
sns.clustermap(data_normalized, cmap='Blues');

data_normalized = pd_crosstab

# Standardize the mean and variance within a stat, so different stats can be comparable
# (This is the same as changing all the columns to Z-scores)
data_normalized = (data_normalized - data_normalized.mean())/data_normalized.var()

# Normalize these values to range from -1 to 1
data_normalized = (data_normalized)/(data_normalized.max() - data_normalized.min())

#data_normalized = data_normalized.T

# Can use a semicolon after the command to suppress output of the row_dendrogram and col_dendrogram.
sns.clustermap(data_normalized, cmap='BuPu');

import matplotlib.pyplot as plt
sns.clustermap(data_normalized);
fig = plt.gcf()
fig.savefig('clusteredheatmap_bbox_tight.png', bbox_inches='tight')

tidy_df = pd.melt(df.reset_index(), id_vars='index')
df.T.head()
0 1 2 3 4 5 6 7 8 9 ... 116 117 118 119 120 121 122 123 124 125
Unnamed: 0 0 1 2 3 4 5 6 7 14 13 ... 128 130 123 125 131 132 133 134 135 136
year 2000 2000 2000 2000 2000 2000 2000 2000 2001 2001 ... 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018
title "Institutions at the Domestic/International Ne... Born to Lose and Doomed to Survive: State Deat... The significance of “allegiance” in internatio... The significance of “allegiance” in internatio... Truth-Telling and Mythmaking in Post-Soviet Ru... Building a Cape Fear Metropolis: Fort Bragg, F... The Glories and the Sadness: Shaping the natio... What leads longstanding adversaries to engage ... A School for the Nation: Military Institution... The 'American Century' Army: The Origins of t... ... Fully Committed? Religiously Committed State P... Straddling the Threshold of Two Worlds: Soldie... U.S. Army’s Investigation and Adjudication of ... Grand Strategic Crucibles: The Lasting Effects... Trust in International Politics: The Role of L... Planning for the Short Haul: Trade Among Belli... Clinging to the Anti-Imperial Mantle: The Repu... The New Navy's Pacific Wars: Peripheral Confl... Stop or I'll Shoot, Comply and I Won't: The Di... Unexpected Humanitarians: Albania, the U.S. Mi...
abstract Civil-military relations are frequently studie... Under what conditions do states die, or exit t... My dissertation employs original and secondary... \nThis study revises prevailing interpretation... Can distorted and pernicious ideas r histo... My dissertation examines the cultural and econ... In my dissertation I compare the ways in whic... This dissertation develops a socio-psychoanal... Beginning in Europe in the latter half of the ... This dissertation covers the period 1949-1959 ... ... This dissertation argues that the higher the l... This dissertation explores how American soldie... This dissertation examines the U.S. Army’s res... When and how do military interventions shape g... In my dissertation, I focus on how leader rela... In times of war, why do belligerents continue ... My dissertation project, Clinging to the Anti-... Using a transnational methodology and sources ... There is a dilemma at the heart of coercion. S... Using archives and oral history, this disserta...
theme IR scholarship IR scholarship IR scholarship Conflit Between States Conflict Between States Domestic Military History Culture Culture / Peace Process Military History Military History ... IR Scholarship Military History Military History IR Scholarship Nuclear Weapons Conflict between states Cold War Military History IR Scholarship Military History

5 rows × 126 columns