{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysing Experimental Data in Table Form with Pandas\n", "\n", "There are (as usually the case with python) many different ways and packages to do data analysis and visualisation in python. The package we will look at in this notebook is called [Pandas](https://pandas.pydata.org) and is probably the most widely used python package for data analysis if the data is in table form. There are many ressources online for free that we encourage you to check out. This notebook will really only scratch the surface of what you can do with pandas. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:30:35.271347Z", "start_time": "2024-01-25T15:30:35.265811Z" } }, "outputs": [], "source": [ "# Import pandas as pd\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading data file\n", "\n", "The file \"data.csv\" contains results from a participant who participated in the gaze cue experiment you programmed today! \n", "using `.read_csv` we can read in our data file and store it under `df`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:31:12.686019Z", "start_time": "2024-01-25T15:31:12.025997Z" } }, "outputs": [], "source": [ "url='https://drive.google.com/file/d/1J9mUX7R0mmuQW3-Hwg3W_QNrjbQdzFv7/view?usp=sharing'\n", "url='https://drive.google.com/uc?id=' + url.split('/')[-2]\n", "df = pd.read_csv(url)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:32:50.482046Z", "start_time": "2024-01-25T15:32:50.477933Z" } }, "outputs": [], "source": [ "df_tutors = pd.DataFrame({\n", " \"Name\": [\"Aylin\",\"Yuri\"],\n", " \"Age\": [27,27]\n", "})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:32:57.147356Z", "start_time": "2024-01-25T15:32:57.135343Z" } }, "outputs": [ { "data": { "text/html": [ "
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NameAge
0Aylin27
1Yuri27
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" ], "text/plain": [ " Name Age\n", "0 Aylin 27\n", "1 Yuri 27" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_tutors" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:33:31.296995Z", "start_time": "2024-01-25T15:33:31.292363Z" } }, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:33:48.878017Z", "start_time": "2024-01-25T15:33:48.864326Z" } }, "outputs": [ { "data": { "text/html": [ "
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subject_idgazetargetpositioncongruencyresponsertcorrect_responsecorrect
0exampleleftHleftcongruentm0.471526m1
1exampleleftFrightincongruenty0.385041y1
2examplerightFrightcongruentm0.315384y0
3examplerightHleftincongruenty0.327810m0
4examplerightHrightcongruentm0.319960m1
5exampleleftFleftcongruentm0.359625y0
6exampleleftFleftcongruenty0.396527y1
7exampleleftFrightincongruentm0.341541y0
8examplerightFrightcongruenty0.423950y1
9exampleleftHleftcongruentm0.325600m1
10exampleleftHrightincongruenty0.326414m0
11examplerightHleftincongruentm0.314234m1
12examplerightHrightcongruenty0.430067m0
13examplerightFleftincongruentm0.310507y0
14exampleleftHrightincongruenty0.394205m0
15examplerightFleftincongruentm0.335534y0
\n", "
" ], "text/plain": [ " subject_id gaze target position congruency response rt \\\n", "0 example left H left congruent m 0.471526 \n", "1 example left F right incongruent y 0.385041 \n", "2 example right F right congruent m 0.315384 \n", "3 example right H left incongruent y 0.327810 \n", "4 example right H right congruent m 0.319960 \n", "5 example left F left congruent m 0.359625 \n", "6 example left F left congruent y 0.396527 \n", "7 example left F right incongruent m 0.341541 \n", "8 example right F right congruent y 0.423950 \n", "9 example left H left congruent m 0.325600 \n", "10 example left H right incongruent y 0.326414 \n", "11 example right H left incongruent m 0.314234 \n", "12 example right H right congruent y 0.430067 \n", "13 example right F left incongruent m 0.310507 \n", "14 example left H right incongruent y 0.394205 \n", "15 example right F left incongruent m 0.335534 \n", "\n", " correct_response correct \n", "0 m 1 \n", "1 y 1 \n", "2 y 0 \n", "3 m 0 \n", "4 m 1 \n", "5 y 0 \n", "6 y 1 \n", "7 y 0 \n", "8 y 1 \n", "9 m 1 \n", "10 m 0 \n", "11 m 1 \n", "12 m 0 \n", "13 y 0 \n", "14 m 0 \n", "15 y 0 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's look at what df looks like!\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, we have 9 columns with 15 rows, each row representing a trial. `Congruency` refers to whether **gaze cue** and **target position** matched in a trial (if yes = congruent, if no = incongruent), the other variables here are pretty self explanatory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data frame slicing\n", "\n", "If you want to look at a specific column you can use square brackets and the column name to *slice* the data frame." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:36:17.012093Z", "start_time": "2024-01-25T15:36:17.005129Z" } }, "outputs": [ { "data": { "text/plain": [ "0 m\n", "1 y\n", "2 m\n", "3 y\n", "4 m\n", "5 m\n", "6 y\n", "7 m\n", "8 y\n", "9 m\n", "10 y\n", "11 m\n", "12 y\n", "13 m\n", "14 y\n", "15 m\n", "Name: response, dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"response\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Slicing: There is a number of ways to slice a data frame i.e. select specific rows and/or columns. We won't go over every single one here but just note that in pandas there is many ways to come to the same conclusion.\n", "
\n", "\n", "For more information on indexing in pandas: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data frame methods, helper functions\n", "\n", "Pandas has a lot of data frame methods that allow us to do basic analyses." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:38:49.673518Z", "start_time": "2024-01-25T15:38:49.635926Z" } }, "outputs": [ { "data": { "text/html": [ "
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rtcorrect
count16.00000016.000000
mean0.3611200.437500
std0.0496470.512348
min0.3105070.000000
25%0.3241900.000000
50%0.3385380.000000
75%0.3947851.000000
max0.4715261.000000
\n", "
" ], "text/plain": [ " rt correct\n", "count 16.000000 16.000000\n", "mean 0.361120 0.437500\n", "std 0.049647 0.512348\n", "min 0.310507 0.000000\n", "25% 0.324190 0.000000\n", "50% 0.338538 0.000000\n", "75% 0.394785 1.000000\n", "max 0.471526 1.000000" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# run this cell and see what it does\n", "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Methods: Dataframe methods can be accessed via giving the name of the dataframe (df) then \".\" and then you can scroll through available methods with \"tab\". Remember to add \"( )\" at the end. There are a lot of useful methods to support basic analyses.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Test out different methods below:**" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:41:03.244066Z", "start_time": "2024-01-25T15:41:03.231273Z" } }, "outputs": [ { "data": { "text/html": [ "
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subject_idgazetargetpositioncongruencyresponsertcorrect_responsecorrect
0exampleleftHleftcongruentm0.471526m1
1exampleleftFrightincongruenty0.385041y1
2examplerightFrightcongruentm0.315384y0
3examplerightHleftincongruenty0.327810m0
4examplerightHrightcongruentm0.319960m1
\n", "
" ], "text/plain": [ " subject_id gaze target position congruency response rt \\\n", "0 example left H left congruent m 0.471526 \n", "1 example left F right incongruent y 0.385041 \n", "2 example right F right congruent m 0.315384 \n", "3 example right H left incongruent y 0.327810 \n", "4 example right H right congruent m 0.319960 \n", "\n", " correct_response correct \n", "0 m 1 \n", "1 y 1 \n", "2 y 0 \n", "3 m 0 \n", "4 m 1 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# shows the first 3 rows - especially useful with large data frames\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:41:28.188574Z", "start_time": "2024-01-25T15:41:28.178628Z" } }, "outputs": [ { "data": { "text/html": [ "
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subject_idgazetargetpositioncongruencyresponsertcorrect_responsecorrect
13examplerightFleftincongruentm0.310507y0
14exampleleftHrightincongruenty0.394205m0
15examplerightFleftincongruentm0.335534y0
\n", "
" ], "text/plain": [ " subject_id gaze target position congruency response rt \\\n", "13 example right F left incongruent m 0.310507 \n", "14 example left H right incongruent y 0.394205 \n", "15 example right F left incongruent m 0.335534 \n", "\n", " correct_response correct \n", "13 y 0 \n", "14 m 0 \n", "15 y 0 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# shows last few rows \n", "df.tail(3)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:41:54.481863Z", "start_time": "2024-01-25T15:41:54.475746Z" } }, "outputs": [ { "data": { "text/plain": [ "0.4375" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# gives you mean for selected column accuracy\n", "df[\"correct\"].mean()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:42:31.341177Z", "start_time": "2024-01-25T15:42:31.331411Z" } }, "outputs": [ { "data": { "text/plain": [ "array(['left', 'right'], dtype=object)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# gives unique entries for column gaze\n", "df[\"gaze\"].unique()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "gaze\n", "left 8\n", "right 8\n", "Name: count, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# counts number of entries per condition for column gaze\n", "df[\"gaze\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These helper functions even include some basic plots!" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:44:19.070746Z", "start_time": "2024-01-25T15:44:18.785833Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# histogram of accuracy\n", "df[\"correct\"].hist()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:44:41.422702Z", "start_time": "2024-01-25T15:44:41.193424Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"rt\"].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparisons\n", "\n", "You can pretty easily add new columns to a data frame (see below)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:50:37.607991Z", "start_time": "2024-01-25T15:50:37.593601Z" } }, "outputs": [ { "data": { "text/html": [ "
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subject_idgazetargetpositioncongruencyresponsertcorrect_responsecorrectslownumber 4
0exampleleftHleftcongruentm0.471526m1True4
1exampleleftFrightincongruenty0.385041y1False4
2examplerightFrightcongruentm0.315384y0False4
3examplerightHleftincongruenty0.327810m0False4
4examplerightHrightcongruentm0.319960m1False4
5exampleleftFleftcongruentm0.359625y0False4
6exampleleftFleftcongruenty0.396527y1False4
7exampleleftFrightincongruentm0.341541y0False4
8examplerightFrightcongruenty0.423950y1True4
9exampleleftHleftcongruentm0.325600m1False4
10exampleleftHrightincongruenty0.326414m0False4
11examplerightHleftincongruentm0.314234m1False4
12examplerightHrightcongruenty0.430067m0True4
13examplerightFleftincongruentm0.310507y0False4
14exampleleftHrightincongruenty0.394205m0False4
15examplerightFleftincongruentm0.335534y0False4
\n", "
" ], "text/plain": [ " subject_id gaze target position congruency response rt \\\n", "0 example left H left congruent m 0.471526 \n", "1 example left F right incongruent y 0.385041 \n", "2 example right F right congruent m 0.315384 \n", "3 example right H left incongruent y 0.327810 \n", "4 example right H right congruent m 0.319960 \n", "5 example left F left congruent m 0.359625 \n", "6 example left F left congruent y 0.396527 \n", "7 example left F right incongruent m 0.341541 \n", "8 example right F right congruent y 0.423950 \n", "9 example left H left congruent m 0.325600 \n", "10 example left H right incongruent y 0.326414 \n", "11 example right H left incongruent m 0.314234 \n", "12 example right H right congruent y 0.430067 \n", "13 example right F left incongruent m 0.310507 \n", "14 example left H right incongruent y 0.394205 \n", "15 example right F left incongruent m 0.335534 \n", "\n", " correct_response correct slow number 4 \n", "0 m 1 True 4 \n", "1 y 1 False 4 \n", "2 y 0 False 4 \n", "3 m 0 False 4 \n", "4 m 1 False 4 \n", "5 y 0 False 4 \n", "6 y 1 False 4 \n", "7 y 0 False 4 \n", "8 y 1 True 4 \n", "9 m 1 False 4 \n", "10 m 0 False 4 \n", "11 m 1 False 4 \n", "12 m 0 True 4 \n", "13 y 0 False 4 \n", "14 m 0 False 4 \n", "15 y 0 False 4 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"number 4\"] = 4\n", "df" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:50:14.570575Z", "start_time": "2024-01-25T15:50:14.552605Z" } }, "outputs": [ { "data": { "text/html": [ "
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subject_idgazetargetpositioncongruencyresponsertcorrect_responsecorrectslow
0exampleleftHleftcongruentm0.471526m1True
1exampleleftFrightincongruenty0.385041y1False
2examplerightFrightcongruentm0.315384y0False
3examplerightHleftincongruenty0.327810m0False
4examplerightHrightcongruentm0.319960m1False
5exampleleftFleftcongruentm0.359625y0False
6exampleleftFleftcongruenty0.396527y1False
7exampleleftFrightincongruentm0.341541y0False
8examplerightFrightcongruenty0.423950y1True
9exampleleftHleftcongruentm0.325600m1False
10exampleleftHrightincongruenty0.326414m0False
11examplerightHleftincongruentm0.314234m1False
12examplerightHrightcongruenty0.430067m0True
13examplerightFleftincongruentm0.310507y0False
14exampleleftHrightincongruenty0.394205m0False
15examplerightFleftincongruentm0.335534y0False
\n", "
" ], "text/plain": [ " subject_id gaze target position congruency response rt \\\n", "0 example left H left congruent m 0.471526 \n", "1 example left F right incongruent y 0.385041 \n", "2 example right F right congruent m 0.315384 \n", "3 example right H left incongruent y 0.327810 \n", "4 example right H right congruent m 0.319960 \n", "5 example left F left congruent m 0.359625 \n", "6 example left F left congruent y 0.396527 \n", "7 example left F right incongruent m 0.341541 \n", "8 example right F right congruent y 0.423950 \n", "9 example left H left congruent m 0.325600 \n", "10 example left H right incongruent y 0.326414 \n", "11 example right H left incongruent m 0.314234 \n", "12 example right H right congruent y 0.430067 \n", "13 example right F left incongruent m 0.310507 \n", "14 example left H right incongruent y 0.394205 \n", "15 example right F left incongruent m 0.335534 \n", "\n", " correct_response correct slow \n", "0 m 1 True \n", "1 y 1 False \n", "2 y 0 False \n", "3 m 0 False \n", "4 m 1 False \n", "5 y 0 False \n", "6 y 1 False \n", "7 y 0 False \n", "8 y 1 True \n", "9 m 1 False \n", "10 m 0 False \n", "11 m 1 False \n", "12 m 0 True \n", "13 y 0 False \n", "14 m 0 False \n", "15 y 0 False " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Run this cell and try to understand what's going on here\n", "df[\"slow\"] = df[\"rt\"] > 0.4\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Create columns: We can create new columns by assigning a single value like this: df[\"number 4\"] = 4 or we could do what we did above which is varying the entry depending on entries in other columns: You could also read it like this: If rt is greater than 0.4, assign \"true\" to slow, else assign False.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Group By\n", "\n", "If we have different groups within our data - in our case for example we have some congruent and some incongruent trials - we can use `groupby` to get some grouped summary statistics for example, mean accuracy per group." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:53:02.572435Z", "start_time": "2024-01-25T15:53:02.558684Z" } }, "outputs": [ { "data": { "text/plain": [ "congruency\n", "congruent 0.625\n", "incongruent 0.250\n", "Name: correct, dtype: float64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(\"congruency\")[\"correct\"].mean()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:53:05.998288Z", "start_time": "2024-01-25T15:53:05.992069Z" } }, "outputs": [ { "data": { "text/plain": [ "congruency\n", "congruent 0.380330\n", "incongruent 0.341911\n", "Name: rt, dtype: float64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(\"congruency\")[\"rt\"].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "instead of computing the mean we can also use groupby to plot grouped histograms:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:53:39.603621Z", "start_time": "2024-01-25T15:53:39.340708Z" } }, "outputs": [ { "data": { "text/plain": [ "congruency\n", "congruent Axes(0.125,0.11;0.775x0.77)\n", "incongruent Axes(0.125,0.11;0.775x0.77)\n", "Name: rt, dtype: object" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.groupby(\"congruency\")[\"rt\"].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualisation with seaborn\n", "\n", "Let's also go over some data visualisation using the widely used package [seaborn](https://seaborn.pydata.org)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:55:44.221613Z", "start_time": "2024-01-25T15:55:43.005582Z" } }, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load an example dataset from seaborn" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:55:59.767496Z", "start_time": "2024-01-25T15:55:59.750431Z" } }, "outputs": [], "source": [ "# Load an example dataset\n", "tips = sns.load_dataset(\"tips\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:56:01.294848Z", "start_time": "2024-01-25T15:56:01.258255Z" } }, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
........................
23929.035.92MaleNoSatDinner3
24027.182.00FemaleYesSatDinner2
24122.672.00MaleYesSatDinner2
24217.821.75MaleNoSatDinner2
24318.783.00FemaleNoThurDinner2
\n", "

244 rows × 7 columns

\n", "
" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4\n", ".. ... ... ... ... ... ... ...\n", "239 29.03 5.92 Male No Sat Dinner 3\n", "240 27.18 2.00 Female Yes Sat Dinner 2\n", "241 22.67 2.00 Male Yes Sat Dinner 2\n", "242 17.82 1.75 Male No Sat Dinner 2\n", "243 18.78 3.00 Female No Thur Dinner 2\n", "\n", "[244 rows x 7 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T15:58:25.322775Z", "start_time": "2024-01-25T15:58:24.124997Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a visualization\n", "sns.relplot(\n", " data=tips,\n", " x=\"total_bill\", y=\"tip\", col=\"time\",\n", " hue=\"smoker\", style=\"smoker\", size=\"size\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default for `relplot` are scatterplots as you can see. But you can easily change it to lineplots using the `kind` argument. Lets try it on a new dataset that is better fitted for a lineplot: timeseries data" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T16:00:48.392764Z", "start_time": "2024-01-25T16:00:48.384834Z" } }, "outputs": [], "source": [ "dots = sns.load_dataset(\"dots\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T16:01:38.064457Z", "start_time": "2024-01-25T16:01:38.052691Z" } }, "outputs": [ { "data": { "text/html": [ "
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alignchoicetimecoherencefiring_rate
0dotsT1-800.033.189967
1dotsT1-803.231.691726
2dotsT1-806.434.279840
3dotsT1-8012.832.631874
4dotsT1-8025.635.060487
..................
843saccT23003.233.281734
844saccT23006.427.583979
845saccT230012.828.511530
846saccT230025.627.009804
847saccT230051.230.959302
\n", "

848 rows × 5 columns

\n", "
" ], "text/plain": [ " align choice time coherence firing_rate\n", "0 dots T1 -80 0.0 33.189967\n", "1 dots T1 -80 3.2 31.691726\n", "2 dots T1 -80 6.4 34.279840\n", "3 dots T1 -80 12.8 32.631874\n", "4 dots T1 -80 25.6 35.060487\n", ".. ... ... ... ... ...\n", "843 sacc T2 300 3.2 33.281734\n", "844 sacc T2 300 6.4 27.583979\n", "845 sacc T2 300 12.8 28.511530\n", "846 sacc T2 300 25.6 27.009804\n", "847 sacc T2 300 51.2 30.959302\n", "\n", "[848 rows x 5 columns]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dots" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T16:01:32.168062Z", "start_time": "2024-01-25T16:01:30.821311Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.relplot(\n", " data=dots, kind=\"line\",\n", " x=\"time\", y=\"firing_rate\", col=\"align\",\n", " hue=\"choice\", size=\"coherence\", style=\"choice\",\n", " facet_kws=dict(sharex=False),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Including statistical estimates: means and confidence intervals" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T16:05:21.180175Z", "start_time": "2024-01-25T16:05:18.657775Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", " with pd.option_context('mode.use_inf_as_na', True):\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fmri = sns.load_dataset(\"fmri\")\n", "sns.relplot(\n", " data=fmri, kind=\"line\",\n", " x=\"timepoint\", y=\"signal\", col=\"region\",\n", " hue=\"event\", style=\"event\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also easily include a linear regression model and its uncertainty using `lmplot`:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T16:06:29.506733Z", "start_time": "2024-01-25T16:06:28.275389Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(data=tips, x=\"total_bill\", y=\"tip\", col=\"time\", hue=\"smoker\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Seaborn plots: As you can see, these plots always follow a very similar structure: define what kind of plot you want (check documentation for available types), define what should be on the x-axis and what should be on the y-axis, provide name of the dataframe.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercises\n", "\n", "1.1 create a boxplot with seaborn for our gaze cue data where you plot reaction time by congruency with the colors white and blue" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T16:22:38.956319Z", "start_time": "2024-01-25T16:22:38.760753Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0cdUAAKA1szQAbdq0SbNmzdLcuXNVVFSkwYMHa/jw4SotLb3ofpWVlRo/fryGDh3qs+3jjz/WmDFjlJqaqv/93/9Vamqq7rvvPv31r39trmkAAIBWxtIAtGzZMk2aNEmTJ09WXFycMjMzFRkZqRUrVlx0v6lTp2rcuHFKSkry2ZaZmak77rhDaWlpio2NVVpamoYOHarMzMwGx6upqVFVVZXXCwAAtF2WBaDa2loVFhYqJSXFqz0lJUUFBQUN7rd69WodOXJECxYsqHf7xx9/7DPmnXfeedExlyxZorCwMM8rMjLyEmYCAABaG8sC0PHjx1VXV6fw8HCv9vDwcFVUVNS7T0lJiebMmaP169crICCg3j4VFRWXNKYkpaWlqbKy0vM6evToJc4GAAC0JvWniKvI4XB4vTfG+LRJUl1dncaNG6eMjAz17du3ScY8z+l0yul0XkLVAACgNbMsAHXp0kX+/v4+KzPHjh3zWcGRpNOnT2vv3r0qKirSjBkzJElut1vGGAUEBGjbtm26/fbbde211zZ6TAAAYE+WnQILDAxUQkKC8vLyvNrz8vI0aNAgn/6hoaHat2+fiouLPa9p06bp+uuvV3FxsQYOHChJSkpK8hlz27Zt9Y4JAADsydJTYLNnz1ZqaqoSExOVlJSk7OxslZaWatq0aZLOXZtTVlamtWvXys/PT/Hx8V77d+vWTS6Xy6v90Ucf1a233qrnn39eI0aM0B//+Ed98MEH2rlz51WdGwAAaLksDUBjxozRiRMntHDhQpWXlys+Pl65ubmKioqSJJWXl//gPYEuNGjQIL355puaN2+e5s+fr969e2vTpk2eFSIAAACHMcZYXURLU1VVpbCwMFVWVio0NLTJx6+urtbo0aMlSTk5OXK5XE3+GUBrw3EB4Epdys9vyx+FAQAAcLURgAAAgO1Yfh8gAEDLY4xRTU2N1WVcke/Pwel0XvR+cK1FW5lHS0AAAgD4qKmp8VyThZaD6+OaDqfAAACA7bACBADw4XQ6lZOTY3UZV6S6ulqpqamSpHXr1rWJlRMe29R0CEAAAB8Oh6NNBIbzXC5Xm5oPrhynwAAAgO0QgAAAgO1cVgDq1auXTpw44dN+6tQp9erV64qLAgAAaE6XFYC+/PJL1dXV+bTX1NSorKzsiosCAABoTpd0EfQ777zj+Xrr1q0KCwvzvK+rq9OHH36o6OjoJisOAACgOVxSABo5cqTn6wkTJnhta9eunaKjo/XSSy81SWEAAADN5ZICkNvtVm1trfr27as33nhDP/3pT5urLgAAgGZzydcABQYG6ptvvlF4eHhz1AMAANDsLusi6PHjx+u1115r6loAAACuisu6E3Rtba1effVV5eXlKTExUcHBwV7bly1b1iTFAQAANIfLCkCffvqpbr75ZknSoUOHvLY5HI4rrwoAAKAZXVYA2r59e1PXAQAAcNXwKAwAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7lgegrKwsxcTEyOVyKSEhQfn5+Q323blzp5KTk9W5c2cFBQUpNjZWL7/8sk+/zMxMXX/99QoKClJkZKQee+wxVVdXN+c0AABAK3JZD0NtKps2bdKsWbOUlZWl5ORkrVy5UsOHD9f+/fvVs2dPn/7BwcGaMWOG+vfvr+DgYO3cuVNTp05VcHCwpkyZIklav3695syZo9dff12DBg3SoUOHNHHiREmqNywBAAD7sTQALVu2TJMmTdLkyZMlnVu52bp1q1asWKElS5b49B8wYIAGDBjgeR8dHa0tW7YoPz/fE4A+/vhjJScna9y4cZ4+Y8eO1SeffHIVZgQAAFoDy06B1dbWqrCwUCkpKV7tKSkpKigoaNQYRUVFKigo0JAhQzxtP/3pT1VYWOgJPJ9//rlyc3N11113NThOTU2NqqqqvF4AAKDtsmwF6Pjx46qrq1N4eLhXe3h4uCoqKi66b48ePfTvf/9bZ8+eVXp6umcFSZLuv/9+/fvf/9ZPf/pTGWN09uxZ/epXv9KcOXMaHG/JkiXKyMi4sgkBAIBWw/KLoB0Oh9d7Y4xP24Xy8/O1d+9evfLKK8rMzNTGjRs923bs2KFFixYpKytLf/vb37Rlyxa99957+s1vftPgeGlpaaqsrPS8jh49emWTAgAALZplK0BdunSRv7+/z2rPsWPHfFaFLhQTEyNJ6tevn77++mulp6dr7NixkqT58+crNTXVsyrUr18/ffPNN5oyZYrmzp0rPz/fzOd0OuV0OptiWpeM304DzuFYAHA1WRaAAgMDlZCQoLy8PI0aNcrTnpeXpxEjRjR6HGOMampqPO+//fZbn5Dj7+8vY4yMMVdeeBNLTU21ugQAAGzH0t8Cmz17tlJTU5WYmKikpCRlZ2ertLRU06ZNk3Tu1FRZWZnWrl0rSVq+fLl69uyp2NhYSefuC7R06VLNnDnTM+bdd9+tZcuWacCAARo4cKAOHz6s+fPn65e//KX8/f2v/iQBAECLY2kAGjNmjE6cOKGFCxeqvLxc8fHxys3NVVRUlCSpvLxcpaWlnv5ut1tpaWn64osvFBAQoN69e+u5557T1KlTPX3mzZsnh8OhefPmqaysTF27dtXdd9+tRYsWXfX5Nca6devkcrmsLgOwXHV1NSuiAK4ah2mJ54UsVlVVpbCwMFVWVio0NLTJx6+urtbo0aMlSTk5OQQgQBwXaHr8nbKfS/n5bflvgQEAAFxtBCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7lgegrKwsxcTEyOVyKSEhQfn5+Q323blzp5KTk9W5c2cFBQUpNjZWL7/8sk+/U6dOafr06YqIiJDL5VJcXJxyc3ObcxoAAKAVCbDywzdt2qRZs2YpKytLycnJWrlypYYPH679+/erZ8+ePv2Dg4M1Y8YM9e/fX8HBwdq5c6emTp2q4OBgTZkyRZJUW1urO+64Q926ddPmzZvVo0cPHT16VCEhIVd7egAAoIWyNAAtW7ZMkyZN0uTJkyVJmZmZ2rp1q1asWKElS5b49B8wYIAGDBjgeR8dHa0tW7YoPz/fE4Bef/11nTx5UgUFBWrXrp0kKSoq6irMBgAAtBaWnQKrra1VYWGhUlJSvNpTUlJUUFDQqDGKiopUUFCgIUOGeNreeecdJSUlafr06QoPD1d8fLwWL16surq6BsepqalRVVWV1wsAALRdlgWg48ePq66uTuHh4V7t4eHhqqiouOi+PXr0kNPpVGJioqZPn+5ZQZKkzz//XJs3b1ZdXZ1yc3M1b948vfTSS1q0aFGD4y1ZskRhYWGeV2Rk5JVNDgAAtGiWngKTJIfD4fXeGOPTdqH8/HydOXNGu3fv1pw5c9SnTx+NHTtWkuR2u9WtWzdlZ2fL399fCQkJ+uqrr/Tiiy/qmWeeqXe8tLQ0zZ492/O+qqqKEAQAQBtmWQDq0qWL/P39fVZ7jh075rMqdKGYmBhJUr9+/fT1118rPT3dE4AiIiLUrl07+fv7e/rHxcWpoqJCtbW1CgwM9BnP6XTK6XRe6ZQAAEArYdkpsMDAQCUkJCgvL8+rPS8vT4MGDWr0OMYY1dTUeN4nJyfr8OHDcrvdnrZDhw4pIiKi3vADAADsx9L7AM2ePVuvvvqqXn/9dR04cECPPfaYSktLNW3aNEnnTk2NHz/e03/58uV69913VVJSopKSEq1evVpLly7Vgw8+6Onzq1/9SidOnNCjjz6qQ4cO6f3339fixYs1ffr0qz4/AADQMll6DdCYMWN04sQJLVy4UOXl5YqPj1dubq7n19bLy8tVWlrq6e92u5WWlqYvvvhCAQEB6t27t5577jlNnTrV0ycyMlLbtm3TY489pv79+6t79+569NFH9dRTT131+QEAgJbJ8ougH3nkET3yyCP1bluzZo3X+5kzZ2rmzJk/OGZSUpJ2797dFOUBAIA2yPJHYQAAAFxtBCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7BCAAAGA7lv8aPAC0JRfenR7Wqa6urvdrWMvpdP7gMz+vBgIQADShmpoajR492uoycIHU1FSrS8D/l5OTI5fLZXUZnAIDAAD2wwoQADSTAwcWy+3mIczWMXI4as99ZQIlWX/axa78/GoVF/e01WV4IQABQDNxuwNljNPqMmzNGOtPtUByu62uwBenwAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO0QgAAAgO1YHoCysrIUExMjl8ulhIQE5efnN9h3586dSk5OVufOnRUUFKTY2Fi9/PLLDfZ/88035XA4NHLkyGaoHAAAtFYBVn74pk2bNGvWLGVlZSk5OVkrV67U8OHDtX//fvXs2dOnf3BwsGbMmKH+/fsrODhYO3fu1NSpUxUcHKwpU6Z49f3nP/+pJ554QoMHD75a0wEAAK2EpStAy5Yt06RJkzR58mTFxcUpMzNTkZGRWrFiRb39BwwYoLFjx+rGG29UdHS0HnzwQd15550+q0Z1dXV64IEHlJGRoV69ev1gHTU1NaqqqvJ6AQCAtsuyAFRbW6vCwkKlpKR4taekpKigoKBRYxQVFamgoEBDhgzxal+4cKG6du2qSZMmNWqcJUuWKCwszPOKjIxs3CQAAECrZFkAOn78uOrq6hQeHu7VHh4eroqKiovu26NHDzmdTiUmJmr69OmaPHmyZ9uuXbv02muvadWqVY2uJS0tTZWVlZ7X0aNHL20yAACgVbH0GiBJcjgcXu+NMT5tF8rPz9eZM2e0e/duzZkzR3369NHYsWN1+vRpPfjgg1q1apW6dOnS6BqcTqecTudl1Q8AAFofywJQly5d5O/v77Pac+zYMZ9VoQvFxMRIkvr166evv/5a6enpGjt2rI4cOaIvv/xSd999t6ev2+2WJAUEBOjgwYPq3bt3E88EAAC0NpadAgsMDFRCQoLy8vK82vPy8jRo0KBGj2OMUU1NjSQpNjZW+/btU3Fxsef1y1/+UrfddpuKi4u5tgcAAEiy+BTY7NmzlZqaqsTERCUlJSk7O1ulpaWaNm2apHPX5pSVlWnt2rWSpOXLl6tnz56KjY2VdO6+QEuXLtXMmTMlSS6XS/Hx8V6fcc0110iSTzsAALAvSwPQmDFjdOLECS1cuFDl5eWKj49Xbm6uoqKiJEnl5eUqLS319He73UpLS9MXX3yhgIAA9e7dW88995ymTp1q1RQAAEArZPlF0I888ogeeeSReretWbPG6/3MmTM9qz2NdeEYAAAAlj8KAwAA4GojAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANux/EaIANBWORw1VpcAtAgt8VggAAFAM7nhhrlWlwCgAZwCAwAAtsMKEAA0k/37F8kYp9VlAJZzOGpa3IooAQgAmokxTgIQ0EJxCgwAANgOAQgAANgOAQgAANgOAQgAANgOAQgAANgOAQgAANgOAQgAANgOAQgAANgOAQgAANgOAQgAANgOj8KwWHV1tdUl2JoxRjU1NZIkp9Mph8NhcUX2xbEA4GoiAFksNTXV6hIAALAdAhAANBM/v1q53VZXYWdGDkftua9MoCRWeK3i51drdQk+CEAWcDqdysnJsboM6Nxpl/OrcOvWrZPL5bK4IkjnjpG2IC7uaatLANAAApAFHA4HP2hbIJfLxZ8LANgEAQgAmhArvC0HK7wtU0tZ4SUAAUATYoW3ZWKFFxey/D5AWVlZiomJkcvlUkJCgvLz8xvsu3PnTiUnJ6tz584KCgpSbGysXn75Za8+q1at0uDBg9WxY0d17NhRw4YN0yeffNLc0wAAAK2IpQFo06ZNmjVrlubOnauioiINHjxYw4cPV2lpab39g4ODNWPGDP3lL3/RgQMHNG/ePM2bN0/Z2dmePjt27NDYsWO1fft2ffzxx+rZs6dSUlJUVlZ2taYFAABaOIcxxlj14QMHDtTNN9+sFStWeNri4uI0cuRILVmypFFj3HPPPQoODta6devq3V5XV6eOHTvqd7/7ncaPH9+oMauqqhQWFqbKykqFhoY2ah+0TtXV1Ro9erQkKScnhyVyoA3h+LafS/n5bdkKUG1trQoLC5WSkuLVnpKSooKCgkaNUVRUpIKCAg0ZMqTBPt9++62+++47derUqcE+NTU1qqqq8noBAIC2y7IAdPz4cdXV1Sk8PNyrPTw8XBUVFRfdt0ePHnI6nUpMTNT06dM1efLkBvvOmTNH3bt317Bhwxrss2TJEoWFhXlekZGRlzYZAADQqlh+EfSFz14yxvzg85jy8/O1d+9evfLKK8rMzNTGjRvr7ffCCy9o48aN2rJly0WXPtPS0lRZWel5HT169NInAgAAWg3Lfg2+S5cu8vf391ntOXbsmM+q0IViYmIkSf369dPXX3+t9PR0jR071qvP0qVLtXjxYn3wwQfq37//RcdzOp0t5r4EAACg+Vm2AhQYGKiEhATl5eV5tefl5WnQoEGNHuf7T/M+78UXX9RvfvMb/fnPf1ZiYmKT1AsAANoOS2+EOHv2bKWmpioxMVFJSUnKzs5WaWmppk2bJuncqamysjKtXbtWkrR8+XL17NlTsbGxks7dF2jp0qWaOXOmZ8wXXnhB8+fP14YNGxQdHe1ZYerQoYM6dOhwlWcIAABaIksD0JgxY3TixAktXLhQ5eXlio+PV25urqKioiRJ5eXlXvcEcrvdSktL0xdffKGAgAD17t1bzz33nKZOnerpk5WVpdraWt17771en7VgwQKlp6dflXkBAICWzdL7ALVU3AfIPrhPCNB2cXzbT6u4DxAAAIBVeBgqLlt9F6C3NtXV1fV+3Zo5nc4fvJUEANgdAQiXraamxrO83BakpqZaXUKTYKkfAH4Yp8AAAIDtsAKEy+Z0OpWTk2N1GVfk+6fx2sqpI27qCQA/jACEy+ZwONrEqZagoCCrSwAAXGWcAgMAALZDAAIAALZDAAIAALZDAAIAALZDAAIAALbDb4EBAHxwp/eWqa3crqMlIAABAHxwp/eWiTu9Nx1OgQEAANthBQgA4IM7vbdM3Om96RCAAAA+uNM72jpOgQEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANshAAEAANvhafD1MMZIkqqqqiyuBAAANNb5n9vnf45fDAGoHqdPn5YkRUZGWlwJAAC4VKdPn1ZYWNhF+zhMY2KSzbjdbn311VcKCQmRw+Gwuhw0s6qqKkVGRuro0aMKDQ21uhwATYjj216MMTp9+rR+9KMfyc/v4lf5sAJUDz8/P/Xo0cPqMnCVhYaG8g8k0EZxfNvHD638nMdF0AAAwHYIQAAAwHYIQLA9p9OpBQsWyOl0Wl0KgCbG8Y2GcBE0AACwHVaAAACA7RCAAACA7RCAAACA7RCAAABX5Gc/+5lmzZpldRnAJeEiaMBiO3bs0G233ab/+7//0zXXXGN1OcAlO3nypNq1a6eQkBCrS2k1Jk6cqFOnTuntt9+2uhTb4k7QQD2+++47tWvXzuoygFahU6dOVpdwxerq6uRwOH7w8QloO/iThuXcbreef/559enTR06nUz179tSiRYskSfv27dPtt9+uoKAgde7cWVOmTNGZM2c8+06cOFEjR47U0qVLFRERoc6dO2v69On67rvvPH3Ky8t11113KSgoSDExMdqwYYOio6OVmZnp6eNwOPTKK69oxIgRCg4O1rPPPqs1a9b4rMi8/fbbPs+He/fdd5WQkCCXy6VevXopIyNDZ8+e9Rr71Vdf1ahRo9S+fXtdd911eueddyRJX375pW677TZJUseOHeVwODRx4sSm+LYCV833T4FFR0dr8eLFeuihhxQSEqKePXsqOzvbq/+//vUv3X///erUqZOCg4OVmJiov/71r57tK1asUO/evRUYGKjrr79e69at89r/YsfUee+8846uu+46BQUF6bbbbtPvf/97ORwOnTp1SpI8x/d7772nG264QU6nU//85z/rPZ03cuRIr+OytrZWTz75pLp3767g4GANHDhQO3bs8Gw/P/bWrVsVFxenDh066Oc//7nKy8slSenp6fr973+vP/7xj3I4HHI4HF774yoxgMWefPJJ07FjR7NmzRpz+PBhk5+fb1atWmW++eYb86Mf/cjcc889Zt++febDDz80MTExZsKECZ59J0yYYEJDQ820adPMgQMHzLvvvmvat29vsrOzPX2GDRtmbrrpJrN7925TWFhohgwZYoKCgszLL7/s6SPJdOvWzbz22mvmyJEj5ssvvzSrV682YWFhXrX+4Q9/MN8/bP785z+b0NBQs2bNGnPkyBGzbds2Ex0dbdLT073G7tGjh9mwYYMpKSkxv/71r02HDh3MiRMnzNmzZ81bb71lJJmDBw+a8vJyc+rUqSb/HgPNaciQIebRRx81xhgTFRVlOnXqZJYvX25KSkrMkiVLjJ+fnzlw4IAxxpjTp0+bXr16mcGDB5v8/HxTUlJiNm3aZAoKCowxxmzZssW0a9fOLF++3Bw8eNC89NJLxt/f33z00Ueez7vYMWWMMV988YVp166deeKJJ8xnn31mNm7caLp3724kmf/7v/8zxhizevVq065dOzNo0CCza9cu89lnn5kzZ854zeW8ESNGeP27M27cODNo0CDzl7/8xRw+fNi8+OKLxul0mkOHDnmNPWzYMLNnzx5TWFho4uLizLhx4zzfg/vuu8/8/Oc/N+Xl5aa8vNzU1NQ09R8LfgABCJaqqqoyTqfTrFq1ymdbdna26dixozlz5oyn7f333zd+fn6moqLCGHMuAEVFRZmzZ896+owePdqMGTPGGGPMgQMHjCSzZ88ez/aSkhIjyScAzZo1y+vzGxOABg8ebBYvXuzVZ926dSYiIsJr7Hnz5nnenzlzxjgcDvOnP/3JGGPM9u3bvf5hBlqbCwPQgw8+6NnmdrtNt27dzIoVK4wxxqxcudKEhIR4wsqFBg0aZB5++GGvttGjR5tf/OIXnvc/dEw99dRTJj4+3muMuXPn+gQgSaa4uLjBuZz3/QB0+PBh43A4TFlZmVefoUOHmrS0NK+xDx8+7Nm+fPlyEx4e7nk/YcIEM2LEiHq/B7g6uAYIljpw4IBqamo0dOjQerf9+Mc/VnBwsKctOTlZbrdbBw8eVHh4uCTpxhtvlL+/v6dPRESE9u3bJ0k6ePCgAgICdPPNN3u29+nTRx07dvT5vMTExEuuv7CwUHv27PGcspPOXUtQXV2tb7/9Vu3bt5ck9e/f37M9ODhYISEhOnbs2CV/HtAafP/vu8Ph0LXXXuv5+15cXKwBAwY0eN3QgQMHNGXKFK+25ORk/fa3v23wMy48pg4ePKhbbrnFq/9//Md/+HxWYGCg1ziN8be//U3GGPXt29ervaamRp07d/a8b9++vXr37u15HxERwTHfwhCAYKmgoKAGtxljfK63Oe/77RderOxwOOR2uz1jNDT2hb4ftCTJz8/Pp9/3ry2Szl2/lJGRoXvuucdnPJfL1agagbbmYn/fL3bMf7//99X3b8EPHff1jXGhoKAgn34/dNy73W75+/ursLDQ6z9ektShQ4eL1tfQv0ewBhdBw1LnL1L88MMPfbbdcMMNKi4u1jfffONp27Vrl/z8/Hz+99WQ2NhYnT17VkVFRZ62w4cPey6EvJiuXbvq9OnTXp9fXFzs1efmm2/WwYMH1adPH59XY3+bJDAwUNK5lSOgrevfv7+Ki4t18uTJerfHxcVp586dXm0FBQWKi4tr9GfExsZqz549Xm179+5t1L5du3b1XKwsnTsuP/30U8/7AQMGqK6uTseOHfM55q+99tpG1xgYGMgxbzECECzlcrn01FNP6cknn9TatWt15MgR7d69W6+99poeeOABuVwuTZgwQZ9++qm2b9+umTNnKjU11XP664fExsZq2LBhmjJlij755BMVFRVpypQp9f7P70IDBw5U+/bt9fTTT+vw4cPasGGD1qxZ49XnmWee0dq1a5Wenq5//OMfOnDggDZt2qR58+Y1+nsQFRUlh8Oh9957T//+97+9fssNaGvGjh2ra6+9ViNHjtSuXbv0+eef66233tLHH38sSfqv//ovrVmzRq+88opKSkq0bNkybdmyRU888USjP2Pq1Kn67LPP9NRTT+nQoUP6n//5H8+x+0PH/e233673339f77//vj777DM98sgjXv9h6tu3rx544AGNHz9eW7Zs0RdffKE9e/bo+eefV25ubqNrjI6O1t///ncdPHhQx48f91ldRvMjAMFy8+fP1+OPP65nnnlGcXFxGjNmjI4dO6b27dtr69atOnnypG655Rbde++9Gjp0qH73u99d0vhr165VeHi4br31Vo0aNUoPP/ywQkJCvE5R1adTp0564403lJubq379+mnjxo1KT0/36nPnnXfqvffeU15enm655Rb95Cc/0bJlyxQVFdXo+rp3766MjAzNmTNH4eHhmjFjxiXND2hNAgMDtW3bNnXr1k2/+MUv1K9fPz333HOe00kjR47Ub3/7W7344ou68cYbtXLlSq1evVo/+9nPGv0ZMTEx2rx5s7Zs2aL+/ftrxYoVmjt3riTJ6XRedN+HHnpIEyZM0Pjx4zVkyBDFxMR4blVx3urVqzV+/Hg9/vjjuv766/XLX/5Sf/3rXxUZGdnoGh9++GFdf/31SkxMVNeuXbVr165G74umwZ2gYTv/+te/FBkZqQ8++KDei68BtD2LFi3SK6+8oqNHj1pdCloILoJGm/fRRx/pzJkz6tevn8rLy/Xkk08qOjpat956q9WlAWgmWVlZuuWWW9S5c2ft2rVLL774Iqur8EIAQpv33Xff6emnn9bnn3+ukJAQDRo0SOvXr+dRF0AbVlJSomeffVYnT55Uz5499fjjjystLc3qstCCcAoMAADYDhdBAwAA2yEAAQAA2yEAAQAA2yEAAQAA2yEAAQAA2yEAAQAA2yEAAQAA2yEAAbANHjgJ4DwCEIBm4Xa79fzzz6tPnz5yOp3q2bOnFi1aJEnat2+fbr/9dgUFBalz586aMmWKzpw549l34sSJGjlypJYuXaqIiAh17txZ06dP9wow5eXluuuuuxQUFKSYmBht2LBB0dHRyszM9PRxOBx65ZVXNGLECAUHB+vZZ5/VmjVrdM0113jV+vbbb/s8Jfzdd99VQkKCXC6XevXqpYyMDJ09e9Zr7FdffVWjRo1S+/btdd111+mdd97xGuMf//iH7rrrLoWGhiokJESDBw/WkSNH9Je//EXt2rVTRUWFV//HH3+cR7QAVwkBCECzSEtL0/PPP6/58+dr//792rBhg8LDw/Xtt9/q5z//uTp27Kg9e/YoJydHH3zwgc9zmrZv364jR45o+/bt+v3vf681a9ZozZo1nu3jx4/XV199pR07duitt95Sdna2jh075lPHggULNGLECO3bt08PPfRQo2rfunWrHnzwQf3617/W/v37tXLlSq1Zs8YT4M7LyMjQfffdp7///e/6xS9+oQceeEAnT56UJJWVlenWW2+Vy+XSRx99pMLCQj300EM6e/asbr31VvXq1Uvr1q3zjHX27Fm98cYb+s///M/GfosBXAkDAE2sqqrKOJ1Os2rVKp9t2dnZpmPHjubMmTOetvfff9/4+fmZiooKY4wxEyZMMFFRUebs2bOePqNHjzZjxowxxhhz4MABI8ns2bPHs72kpMRIMi+//LKnTZKZNWuW1+evXr3ahIWFebX94Q9/MN//53Dw4MFm8eLFXn3WrVtnIiIivMaeN2+e5/2ZM2eMw+Ewf/rTn4wxxqSlpZmYmBhTW1tb7/fo+eefN3FxcZ73b7/9tunQoYPX9wVA82EFCECTO3DggGpqajR06NB6t/34xz9WcHCwpy05OVlut1sHDx70tN14443y9/f3vI+IiPCs8Bw8eFABAQG6+eabPdv79Omjjh07+nxeYmLiJddfWFiohQsXqkOHDp7Xww8/rPLycn377beefv379/d8HRwcrJCQEE+NxcXFGjx4cIMP3Z04caIOHz6s3bt3S5Jef/113XfffV7fFwDNh6fBA2hyQUFBDW4zxvhcb3Pe99svDA4Oh0Nut9szRkNjX+jCQOHn5+fT78KLo91utzIyMnTPPff4jOdyuRpV48W+B5LUrVs33X333Vq9erV69eql3Nxc7dix46L7AGg6rAABaHLXXXedgoKC9OGHH/psu+GGG1RcXKxvvvnG07Zr1y75+fmpb9++jRo/NjZWZ8+eVVFRkaft8OHDOnXq1A/u27VrV50+fdrr84uLi7363HzzzTp48KD69Onj8/Lza9w/m/3791d+fv5Ff/Ns8uTJevPNN7Vy5Ur17t1bycnJjRobwJUjAAFoci6XS0899ZSefPJJrV27VkeOHNHu3bv12muv6YEHHpDL5dKECRP06aefavv27Zo5c6ZSU1MVHh7eqPFjY2M1bNgwTZkyRZ988omKioo0ZcoUBQUFNbi6dN7AgQPVvn17Pf300zp8+LA2bNjgdXG1JD3zzDNau3at0tPT9Y9//EMHDhzQpk2bNG/evEZ/D2bMmKGqqirdf//92rt3r0pKSrRu3Tqv03x33nmnwsLC9Oyzz3LxM3CVEYAANIv58+fr8ccf1zPPPKO4uDiNGTNGx44dU/v27bV161adPHlSt9xyi+69914NHTpUv/vd7y5p/LVr1yo8PFy33nqrRo0apYcfflghISFep6jq06lTJ73xxhvKzc1Vv379tHHjRqWnp3v1ufPOO/Xee+8pLy9Pt9xyi37yk59o2bJlioqKanR9nTt31kcffaQzZ85oyJAhSkhI0KpVq7xOm/n5+WnixImqq6vT+PHjL2n+AK6MwzR0Mh0AWpF//etfioyM1AcffFDvxdct1cMPP6yvv/7a5x5CAJoXF0EDaJXOr67069dP5eXlevLJJxUdHd1qbiRYWVmpPXv2aP369frjH/9odTmA7RCAALRK3333nZ5++ml9/vnnCgkJ0aBBg7R+/foGf+28pRkxYoQ++eQTTZ06VXfccYfV5QC2wykwAABgO1wEDQAAbIcABAAAbIcABAAAbIcABAAAbIcABAAAbIcABAAAbIcABAAAbIcABAAAbOf/AQthNbG9gaSAAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x=\"congruency\", y=\"rt\",\n", " palette=[\"w\", \"b\"],\n", " data=df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.2 Do the same with a barplot, this time using colors of your choice" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2024-01-25T16:23:02.477504Z", "start_time": "2024-01-25T16:23:02.312826Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/aylinkallmayer/anaconda3/envs/pfp_2023-24/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7d+/WrFmzNHjwYF24cME+V12fYcOGaePGjerdu7f69u2rAwcOaOPGjZo3b54TVwoAAForp4YhSUpLS1NNTY3mz5+vq1evKiIiQllZWTKbzTp37pzi4+O1ZMkSJSYmateuXZKkpUuXaunSpQ7z1PWZMWOGfHx8tHz5chUVFalnz56aN2+eHnjgAWcsDwAAtHImm81mc3YRrV3dAeqmDllfD5WVb8tq5VwTUMfFpaM8PYc6u4zrYt+JUl2qrHF2GUCr0cnTTXf1a9njK839+u30X8cBAADgTIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaIQhAABgaE4PQ1arVStXrlRMTIxCQ0OVnJysgoKCRvufPXtWjzzyiCIjIzVkyBClpaXp/PnzDn327Nmj4cOHq3///ho5cqTee++9ll4GAABoo5wehjIzM7Vt2zYtWrRI27dvl8lkUkpKiqqrq+v1LS0t1aRJk9ShQwdt3bpVGzZsUGlpqR5++GFZLBZJ0sGDBzVz5kyNHTtWO3fuVHR0tKZMmaL8/PwfemkAAKANcGoYqq6u1qZNm5SamqrY2FgFBwcrIyNDxcXF2rt3b73++/btU1VVlZ599lnddttt6tevn5YtW6b8/Hzl5uZKkjZs2KCEhAQlJSUpMDBQs2fPVt++fbVly5YfenkAAKANaJEwVFNT06x+eXl5qqioUFRUlL3Nx8dHISEhOnz4cL3+Q4YM0Zo1a+Tu7l7vWllZmaxWq3Jzcx3mk6TIyEgdOXLkGlcBAACMwO37DIqPj9eaNWsUHBxc79rHH3+slJQU5eTkfOc8RUVFkqTu3bs7tPv7+6uwsLBe/549e6pnz54ObevXr5e7u7siIiJUXl6uyspKBQQENGu+b6+pMYWFhfVqBAAAN4Zmh6Fdu3bZd3y+/PJL/e1vf1NeXl69fgcOHNBXX33VrDmrqqokSWaz2aHd3d1dZWVl3zn+xRdf1Msvv6w5c+aoS5cu9nDV0Hx1Z4oAAAC+qdlh6MSJE3rhhRckSSaTSZmZmY32nTRpUrPm9PDwkPT12aG6P0uSxWJR+/btGx1ns9m0YsUKrV27Vo8++qgmTpwoSfbHZ98+fP1d80lSdnZ2o9ea2jUCAABtW7PD0PTp0zVu3DjZbDbdddddmjZtmn7+85879HF1dZWXl5e8vLyaNWfdo6eSkhL16tXL3l5SUtLgIzhJ+uqrrzRnzhzt2rVLs2bN0uTJk+3XOnXqJE9PT5WUlDiMKSkpqffoDAAAQLqGMGQ2m9WjRw9JUocOHdSvXz/737+v4OBgeXl5KScnxx6GysvLdfLkSSUlJTU4ZtasWdq7d6+WL1+uESNGOFwzmUwKCwvToUOHdP/999vbc3JyNGjQoP+oVgAAcGP6Xgeo+/fvr/379+v222//j25uNpuVlJSk9PR0+fr6qkePHlq2bJkCAgKUkJCg2tpaXbx4Ud7e3vLw8NAbb7yh3bt3a9asWRo8eLAuXLhgn6uuz6RJk/TII48oJCREd9xxh15//XWdOnVKzzzzzH9UKwAAuDF9rzAUFBSkl156SX/961916623qkuXLg7XTSaTFi9e3Ky50tLSVFNTo/nz5+vq1auKiIhQVlaWzGazzp07p/j4eC1ZskSJiYnatWuXJGnp0qVaunSpwzx1faKjo7V48WJlZmYqIyNDt956q9atW6fAwMDvs1QAAHCDM9lsNtu1DoqLi2t6UpOpyQPJbU3dAeqWXlNl5duyWr/7u+gAo3Bx6ShPz6HOLuO62HeiVJcqm/cz2AAj6OTpprv6dW7RezT36/f32hn6+9///n2GAQAAtDpO/91kAAAAzkQYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhkYYAgAAhub0MGS1WrVy5UrFxMQoNDRUycnJKigoaNa4yZMna9WqVfWuxcXFKSgoyOHj8ccfb4nyAQBAG+fm7AIyMzO1bds2LVmyRN26ddOyZcuUkpKiXbt2yWw2Nzjm6tWrmjdvnvbv36+f/OQnDteuXLmi8+fPa/369erbt6+93cPDoyWXAQAA2iin7gxVV1dr06ZNSk1NVWxsrIKDg5WRkaHi4mLt3bu3wTG5ubkaPXq0PvroI/n4+NS7fubMGdlsNoWFhcnPz8/+4e3t3dLLAQAAbZBTw1BeXp4qKioUFRVlb/Px8VFISIgOHz7c4Jj3339fCQkJ2rlzZ4MB5/Tp0/Lz82swKAEAAHybUx+TFRUVSZK6d+/u0O7v76/CwsIGx0ydOrXJOc+cOSNPT0+lpqbq2LFj8vX1VWJiosaPHy8Xl8azX3x8fKPXCgsL69UIAABuDE7dGaqqqpKkemeD3N3dZbFYvtecZ8+e1eXLlzV8+HBlZWXpwQcf1IoVKxo8aA0AAODUnaG6Q83V1dUOB5wtFovat2//vebcvHmzLBaLvLy8JElBQUGqqKjQ2rVrlZqa2ujuUHZ2dqNzNrVrBAAA2jan7gzVPXoqKSlxaC8pKVFAQMD3mrNdu3b2IFSnT58+qqysVFlZ2fcrFAAA3LCcGoaCg4Pl5eWlnJwce1t5eblOnjyp8PDwa57ParUqLi5Oa9eudWg/fvy4unbtqs6dO//HNQMAgBuLUx+Tmc1mJSUlKT09Xb6+vurRo4eWLVumgIAAJSQkqLa2VhcvXpS3t3ezfk6Qi4uLhg0bpo0bN6p3797q27evDhw4oI0bN2revHk/wIoAAEBb4/QfupiWlqaamhrNnz9fV69eVUREhLKysmQ2m3Xu3DnFx8dryZIlSkxMbNZ8M2bMkI+Pj5YvX66ioiL17NlT8+bN0wMPPNDCKwEAAG2RyWaz2ZxdRGtXd4C6qUPW10Nl5duyWjnXBNRxcekoT8+hzi7juth3olSXKmucXQbQanTydNNd/Vr2+Epzv347/XeTAQAAOBNhCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGBphCAAAGJrTw5DVatXKlSsVExOj0NBQJScnq6CgoFnjJk+erFWrVtW7tmfPHg0fPlz9+/fXyJEj9d5777VE6QAA4Abg9DCUmZmpbdu2adGiRdq+fbtMJpNSUlJUXV3d6JirV69q5syZ2r9/f71rBw8e1MyZMzV27Fjt3LlT0dHRmjJlivLz81tyGQAAoI1yahiqrq7Wpk2blJqaqtjYWAUHBysjI0PFxcXau3dvg2Nyc3M1evRoffTRR/Lx8al3fcOGDUpISFBSUpICAwM1e/Zs9e3bV1u2bGnp5QAAgDbIqWEoLy9PFRUVioqKsrf5+PgoJCREhw8fbnDM+++/r4SEBO3cuVPe3t4O16xWq3Jzcx3mk6TIyEgdOXLk+i8AAAC0eW7OvHlRUZEkqXv37g7t/v7+KiwsbHDM1KlTG52vvLxclZWVCggIaPZ8deLj4xu9VlhYWK9GAABwY3DqzlBVVZUkyWw2O7S7u7vLYrFc83xXr169rvMBAIAbn1N3hjw8PCR9fXao7s+SZLFY1L59+2uez93d3T7fNzVnvuzs7EavNbVrBAAA2jan7gzVPXoqKSlxaC8pKan3qKs5OnXqJE9Pz+s2HwAAuPE5NQwFBwfLy8tLOTk59rby8nKdPHlS4eHh1zyfyWRSWFiYDh065NCek5OjQYMG/cf1AgCAG49TH5OZzWYlJSUpPT1dvr6+6tGjh5YtW6aAgAAlJCSotrZWFy9elLe3t8NjtKZMmjRJjzzyiEJCQnTHHXfo9ddf16lTp/TMM8+08GoAAEBb5PQfupiWlqYxY8Zo/vz5euihh+Tq6qqsrCyZzWYVFhYqOjpau3fvbvZ80dHRWrx4sV555RWNHj1aBw8e1Lp16xQYGNiCqwAAAG2VyWaz2ZxdRGtXd4C6qUPW10Nl5duyWsta9B5AW+Li0lGenkOdXcZ1se9EqS5V1ji7DKDV6OTpprv6dW7RezT367fTd4YAAACciTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMjTAEAAAMzelhyGq1auXKlYqJiVFoaKiSk5NVUFDQaP/S0lLNmDFDERERioiI0G9+8xtVVlY69ImLi1NQUJDDx+OPP97SSwEAAG2Qm7MLyMzM1LZt27RkyRJ169ZNy5YtU0pKinbt2iWz2Vyvf1pamiwWi1544QWVl5dr3rx5euqpp/S73/1OknTlyhWdP39e69evV9++fe3jPDw8frA1AQCAtsOpO0PV1dXatGmTUlNTFRsbq+DgYGVkZKi4uFh79+6t1//YsWM6dOiQlixZor59+2rIkCF6+umn9eabb6q4uFiSdObMGdlsNoWFhcnPz8/+4e3t/UMvDwAAtAFODUN5eXmqqKhQVFSUvc3Hx0chISE6fPhwvf5HjhyRn5+fAgMD7W2DBw+WyWTS0aNHJUmnT5+Wn5+ffHx8Wn4BAACgzXPqY7KioiJJUvfu3R3a/f39VVhYWK9/cXFxvb5ms1mdOnWy9z9z5ow8PT2VmpqqY8eOydfXV4mJiRo/frxcXBrPfvHx8Y1eKywsrHdfAABwY3DqzlBVVZUk1Tsb5O7uLovF0mD/hs4RfbP/2bNndfnyZQ0fPlxZWVl68MEHtWLFCq1ataoFVgAAANo6p+4M1R1qrq6udjjgbLFY1L59+wb7V1dX12u3WCzy9PSUJG3evFkWi0VeXl6SpKCgIFVUVGjt2rVKTU1tdHcoOzu70Tqb2jUCAABtm1N3huoePZWUlDi0l5SUKCAgoF7/gICAen2rq6t16dIldevWTZLUrl07exCq06dPH1VWVqqsrOx6lg8AAG4ATg1DwcHB8vLyUk5Ojr2tvLxcJ0+eVHh4eL3+ERERKioqcvg5RHVjw8LCZLVaFRcXp7Vr1zqMO378uLp27arOnTu30EoAAEBb5dTHZGazWUlJSUpPT5evr6969OihZcuWKSAgQAkJCaqtrdXFixfl7e0tDw8PhYaGKiwsTNOmTdOTTz6pyspKLVy4UKNGjbLvDA0bNkwbN25U79691bdvXx04cEAbN27UvHnznLlUAADQSjn9hy6mpaWppqZG8+fP19WrVxUREaGsrCyZzWadO3dO8fHxWrJkiRITE2UymbR69Wo99dRTmjBhgtzd3fXTn/5Uc+bMsc83Y8YM+fj4aPny5SoqKlLPnj01b948PfDAA05cJQAAaK1MNpvN5uwiWru6A9RNHbK+Hior35bVyrkmoI6LS0d5eg51dhnXxb4TpbpUWePsMoBWo5Onm+7q17LHV5r79dvpv5sMAADAmQhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0AhDAADA0JwehqxWq1auXKmYmBiFhoYqOTlZBQUFjfYvLS3VjBkzFBERoYiICP3mN79RZWWlQ589e/Zo+PDh6t+/v0aOHKn33nuvpZcBAADaKKeHoczMTG3btk2LFi3S9u3bZTKZlJKSourq6gb7p6Wl6YsvvtALL7yglStX6oMPPtBTTz1lv37w4EHNnDlTY8eO1c6dOxUdHa0pU6YoPz//h1oSAABoQ5wahqqrq7Vp0yalpqYqNjZWwcHBysjIUHFxsfbu3Vuv/7Fjx3To0CEtWbJEffv21ZAhQ/T000/rzTffVHFxsSRpw4YNSkhIUFJSkgIDAzV79mz17dtXW7Zs+aGXBwAA2gCnhqG8vDxVVFQoKirK3ubj46OQkBAdPny4Xv8jR47Iz89PgYGB9rbBgwfLZDLp6NGjslqtys3NdZhPkiIjI3XkyJGWWwgAAGiz3Jx586KiIklS9+7dHdr9/f1VWFhYr39xcXG9vmazWZ06dVJhYaHKy8tVWVmpgICAZs33TfHx8Y1eO3funFxdXZvscz3YbBZJtha9B9C2mGQyuTu7iOvCUmOV1ersKoDWw8VFWuLWsnsyhYWFcnV1/c5+Tg1DVVVVkr4ONN/k7u6usrKyBvt/u29df4vFoqtXrzY6n8Vi+d51mkwmubm1/KfqRnnTb+vqgvO3gzfwn3Bv4Td9NA+vb2Nxc3NrMDfU6/cD1NIoDw8PSV+fHar7syRZLBa1b9++wf4NHay2WCzy9PSUu7u7fb5vX29ovm/Kzs6+5vpxY6rbAeTfBHDj4fWNhjj1vyp1ybykpMShvaSkpN6jLkkKCAio17e6ulqXLl1St27d1KlTJ3l6ejZ7PgAAAKeGoeDgYHl5eSknJ8feVl5erpMnTyo8PLxe/4iICBUVFTn8HKK6sWFhYTKZTAoLC9OhQ4ccxuXk5GjQoEEttAoAANCWOfUxmdlsVlJSktLT0+Xr66sePXpo2bJlCggIUEJCgmpra3Xx4kV5e3vLw8NDoaGhCgsL07Rp0/Tkk0+qsrJSCxcu1KhRo9StWzdJ0qRJk/TII48oJCREd9xxh15//XWdOnVKzzzzjDOXCgAAWimnn+hLS0vTmDFjNH/+fD300ENydXVVVlaWzGazCgsLFR0drd27d0v6+iDz6tWr1bNnT02YMEGPPfaY7rjjDj355JP2+aKjo7V48WK98sorGj16tA4ePKh169Y5fDs+AABAHZPNZuN7uYFv4IAlcOPi9Y2GOH1nCAAAwJnYGQIAAIbGzhAAADA0whAAADA0whAAADA0whAAADA0whAAADA0whDQipw9e1bvvPOOs8sA/mNBQUF64403nF1Gm3H06FEdOXLE2WUYFmEIaEUeffRRHT9+3NllAP+x/fv3a/jw4c4uo80YO3asPv/8c2eXYVhO/d1kAIAbk5+fn7NLAJqNnSG0OpWVlVq0aJGio6M1cOBA/fKXv9THH38sSTp27JjGjx+vQYMGKTIyUnPnzlVZWZl9bFxcnJ5//nmlpqZq4MCBioyM1OLFi1VTU2Pvs3//fiUmJmrAgAEaMWKEXnvtNQUFBencuXP2ORYvXqzhw4crMjJSBw8e1Lhx4/TEE0841PnEE09o3Lhx9r8XFxdr2rRpCg8PV2RkpH71q1/ps88+c+g/c+ZM/e53v9OQIUMUGhqq//mf/9GFCxfs9/3yyy+1evVqh3mBtuibj8m+69++JF28eFGzZ89WZGSkBg0apJSUFIfXzzvvvKMHHnhAAwcOVHR0tJ599llZLBaH+7366quaNGmSBgwYoJiYGK1fv96hpj//+c/62c9+pv79+2vMmDHasmWLgoKCHObIyMjQ0KFDdfvtt+uTTz5RXFycVq1a5TDPt98P8vPzlZKSYq9txowZDmsbN26cfve732nu3LkKDw9XWFiYZs+erYqKCvt9JWnOnDn13mfwwyAModWZNm2a3n77bS1evFg7d+7ULbfcosmTJ+vjjz/WuHHjdOutt2r79u1auXKlPv74YyUnJ8tqtdrHr1q1ShEREdqxY4dSU1P14osvateuXZKkU6dO6dFHH1VUVJR27typKVOmaOnSpfVqeOWVVzR//nxt3LhRYWFh31lzZWWlxo0bp9raWm3dulUvvfSSOnfurAceeEDFxcX2fnv27NGlS5e0detWrV69WkePHlVGRoYk6bXXXlNAQICSk5PrvfkCbV1T//ZramqUnJysM2fOaM2aNXr11Vfl6uqq5ORk1dTUaN++ffr1r3+t2NhYvf766/rtb3+rPXv26PHHH3e4x9KlSzVq1Ci9+eabuu+++/Tcc8/Zz+G8/fbbmj17tsaMGaM//elPuu+++7R8+fJ6dda9t6xZs0Y//vGPv3NdxcXFGjt2rG666Sa99tprWrduna5cuaJf/OIXqqystPd76aWX1LVrV/3xj3/UokWLtHv3br3wwguSvv4PmiTNnTtX8+bN+16fX/xneEyGVuXTTz/VO++8o40bNyomJkaStGDBAnXo0EHr169XUFCQFixYIEm69dZbtXz5ct1zzz16//33FRsbK0mKiYnR+PHjJUm9e/fWa6+9ptzcXI0aNUovvPCC+vXrp1mzZkmSfvzjH+vf//63Fi1a5FBHbGys/vu//7vZdf/lL39RaWmpli9frnbt2kmSnnnmGeXk5OjVV19VamqqJMnLy0tPP/202rVrp8DAQN1777169913JUm+vr5ydXWVp6enOnXq9D0/g0Dr1NS//YMHD+rUqVPas2ePPYD89re/VVZWli5duqT169crISFBU6ZMkfT169Zms+nXv/618vPzFRgYKEkaPXq07r33XknSY489ppdffllHjx5VeHi4srKy9NOf/lSTJ0+WJN1yyy0qKCjQ5s2bHeq899571b9//2av65VXXpG/v7/9fUmSfv/73ysqKkpvvfWWEhMTJUmBgYGaPn26/d5/+ctflJubK+n/P1L09vaWt7f3NXxWcb0QhtCqnD59WpL0k5/8xN5mNps1Z84cDR8+XLfffrtD/6CgIPn4+Oj06dP2MFT3xljH29tbX331lSTp5MmT9UJOeHh4vTpuvvnma6r75MmTunLligYPHuzQbrFYlJ+f7zBvXVj6dm3Ajaypf/unT5+Wj4+Pw06Mn5+f/ZHRmTNnNGLECIf5IiIi7GPrXvPffu17eXnZ7/GPf/xDd999t8P18PDwemHo+7z28/PzNXDgQIf2b7/2G3pfKi8vv6Z7oeUQhtCquLl9/U/SZDLVu2az2Rpst1qtDm+yZrO5wbGS5Orq6vBIrTEeHh6NzlHnmyHGarXqlltu0dq1a+uN8/T0bLI2wAia+rfv5ubW4Gu7TkOv/draWvvYpu5R97p1c3Nrsdd+VFSUFi5cWG/cN3d5eO23bpwZQqtS97+nb357eU1Nje6880598cUX9X4OR15enq5cuVLvf12NCQ4O1kcffeTQ9u2/N6Rdu3a6fPmyQ9s3vw22T58+On/+vLy9vXXzzTfr5ptvVo8ePbR8+XIdPny4WbUBRnXrrbeqrKxMBQUF9raLFy8qIiJCR48eVZ8+fXT06FGHMXXvBT/0a99qtdq/2UKSbrvtNuXn56t79+72137Hjh21ePFinTlzplm1wfkIQ2hVbrnlFt1999166qmndODAAX366adasGCBqqur9cILLygvL09PP/208vPzdejQIT3++OMKCQnRkCFDmjV/cnKyTpw4ofT0dH366afat2+fVqxYIanh3ag6YWFh+r//+z/9/e9/1xdffKGVK1c6vNHdc8896tixo/73f/9XH374ofLz8zVnzhy9++67uu2225q9/g4dOuizzz7Tv/71r2aPAdq6IUOG2M/yffTRRzp79qzmzJmjLl26qH///po8ebL+9re/ac2aNfr000/19ttv67e//a2GDh3a7DCUkpKiv/71r9q8ebMKCgq0Y8cOvfTSS985LiwsTLt379bhw4f16aef6sknn3QIR2PHjtXly5c1ffp0nTp1Snl5eZoxY4Y+/vjja3rte3p6Kj8/X6Wlpc0eg+uHMIRWZ8mSJRo8eLCmTZumxMREnT9/Xps2bVJ4eLg2bNigEydOaNSoUZo6daoGDhyozZs3Ozwma0qfPn20evVqvfPOOxo5cqRWrFihX/7yl5LU5BwTJ07UsGHDNHPmTI0ePVr/+te/NHHiRPt1b29vbd26VV26dNHDDz+sMWPG6Msvv1RWVtY1vSGOGzdO77zzjpKTk5s9BmjrXFxclJmZqR/96EeaPHmyHnroIbm5uSkrK0tms1k/+9nPlJ6errfeeksjR47UwoULNWLECP3+979v9j3uuOMOPfXUU/rDH/6gESNGaPv27frFL37xne8d06ZNU2hoqFJSUjR27Fh16tTJ4YdJ3nTTTdq6dauqqqo0duxYJSUlyWQyacuWLerSpUuz60tOTtbWrVs1d+7cZo/B9WOyffthKHAD+/jjj+Xm5qaQkBB725///GfNnTtXx44dczh/AODGcejQIXXt2tXhkPa6dev02muvad++fU6sDK0BO0MwlLy8PI0fP17Z2dk6f/68Dhw4oFWrVmnEiBEEIeAG9sEHH2jy5Mk6ePCgzp8/r+zsbG3ZssX+rfgwNnaGYCg2m01r1qzRjh07VFxcrC5dumjEiBFKS0tr8LtIANwYqqurtXTpUv3tb3/TxYsX1b17d40ZM0YPP/ywXF1dnV0enIwwBAAADI3HZAAAwNAIQwAAwNAIQwAAwNAIQwAAwNAIQwAAwNAIQwAAwNAIQwAAwNAIQwBajM1ms/8uqAEDBighIUEbNmxQ3Y83++CDDzR27FgNGjRIkZGRmjFjhgoLC+3j33jjDYWEhOijjz7Sgw8+qP79++vOO+/Uhg0bHO5TUlKiadOmafDgwYqIiNCCBQuUkZGhuLg4e5+4uDgtXrxYEyZMUFhYmBYsWKCcnBwFBQUpJyfHYb5x48Zp3LhxDm1//OMfNWLECPXr10933nmnVq1apZqaGvv1J554QhMnTtTrr7+uYcOGqV+/frrnnnv07rvvOszz+eefKy0tzV5rSkqKzp49K0m677779Itf/KLe53Hy5Mn16gFw/RCGALSY5557Ts8884xiY2O1du1a3X///crIyFBmZqbefPNNJScnq1u3bnruuec0Z84cHTt2TA8++KD+/e9/2+ewWq167LHHNHz4cD3//PMaNGiQ0tPT9f7770v6+icLT5gwQbm5uZo7d66WLFmivLw8bdq0qV49f/jDHxQUFKRVq1Zd069hWL9+vX7zm99oyJAhWrdunX75y19qw4YNWrBggUO/EydOKCsrS2lpaVqzZo3c3NyUlpamsrIySV+Htvvvv1+ffPKJFi5cqPT0dJWVlWnixIm6ePGixowZo2PHjqmgoMA+Z3FxsQ4cOKD77rvvmj73AK6BDQBaQFlZma1v3762xYsXO7QvWbLENmnSJNvtt99umzhxosO1goICW9++fW1Lly612Ww22+uvv27r06eP7dVXX7X3sVgstv79+9uefvppm81ms/3xj3+09enTx3b8+HF7n8uXL9siIyNtQ4cOtbcNHTrUduedd9pqa2vtbQcPHrT16dPHdvDgQYc6kpKSbElJSTabzWYrLy+3hYaG2hYsWODQ59VXX7X16dPHdubMGZvNZrPNnj3b1qdPH1tBQYG9z6FDh2x9+vSxvfXWWzabzWZ79tlnbQMGDLCVlJTY+xQXF9vuvPNOW3Z2tq28vNw2YMAA24oVK+zXn3/+edvAgQNtFRUVDX+iAfzH2BkC0CI+/PBDffXVV0pISHBof+KJJzRv3jxduHBBI0eOdLjWq1cvDRw4sN5jq4EDB9r/bDab5evrq8rKSknSwYMHddNNN6lfv372Pl5eXho6dGi9mgIDA+Xicm1ve8eOHVNVVZXi4uJUU1Nj/6h7BPfBBx/Y+/r6+qpXr172vwcEBEiSqqqqJElHjx7VT37yE/n5+dn7+Pv76+2331ZcXJy8vb119913609/+pP9+s6dO/XTn/5Unp6e11Q3gObj13QDaBGXLl2S9HVAaOxa165d613r2rWrTp486dD27V+i6+LiYj93VFpaqi5dujQ4T3PavktdrY888kiD10tKSux/bt++vcM1k8kk6etHfXVz9ezZs8n7jRkzRn/605905MgRmc1m/fOf/9RTTz11zXUDaD7CEIAW4ePjI0m6ePGifvzjH9vbCwsLdfr0aUnSv/71r3rjLly4oM6dOzf7Pt26dXM4Y1Pnm+eOGvPtsFKnoqJCHTp0kPT/15Genq7evXvXm+NaApa3t7cuXrxYr/3AgQPq2bOnbrrpJg0ePFi9evXSW2+9pXbt2unmm29WeHh4s+8B4NrxmAxAixgwYIDatWun7Oxsh/YtW7bo97//vfz8/PTnP//Z4doXX3yhDz/8UGFhYc2+z+DBg/XFF1/o1KlT9jaLxaL33nvvO8d6eXlJksN3sJWVlSk/P9/+99DQULVr107FxcXq37+//aNdu3Zavny5zp071+xaw8PD9eGHHzoEtYsXLyolJcX+eTKZTEpMTNS+ffu0b98+jR49utnzA/h+2BkC0CJ8fX01fvx4bdmyRWazWVFRUTp+/Li2bt2q6dOnq1OnTpozZ46mTZumUaNGqbS0VKtXr1bHjh01adKkZt/n5z//uZ5//nlNmTJFU6dOlY+PjzZt2qR///vf+tGPftTk2KCgIHXv3l2rV6+Wt7e3XFxc9Pzzzzs87urcubMefvhhrVixQleuXFFkZKSKi4u1YsUKmUwmBQcHN7vWiRMnaufOnZo8ebJ+9atfyd3dXevXr5e/v79GjRpl75eYmKhVq1bJZrM5tANoGYQhAC1m5syZ6tq1q1555RVt2rRJPXv21Ny5czV27FhJUocOHbR+/XpNmTJFXl5eiomJ0fTp0x0OGH8XNzc3ZWVl6ZlnntGTTz4pNzc33XPPPercubM+/fTTJse6urpq5cqVWrx4saZPn66uXbtqwoQJ+uSTTxzGPvbYY/Lz89PLL7+sjRs3qmPHjhoyZIimT58ub2/vZtfavXt3vfzyy1q2bJnmzJkjs9mswYMHa9myZerUqZO9X7du3RQcHKzOnTure/fuzZ4fwPdjstWdQgSANujs2bP65JNPdPfdd9vPAElf/wDDul2ftqa4uFhxcXF67rnnNGzYMGeXA9zw2BkC0KZVVlZq6tSpGjt2rBISElRbW6tdu3bpH//4h2bOnOns8q7JqVOnlJ2drb/+9a/q2bOn7rrrLmeXBBgCO0MA2ry33npLWVlZys/Pl81mU0hIiH79618rOjra2aVdkw8//FCTJ09Wt27dtHz5cv3Xf/2Xs0sCDIEwBAAADI1vrQcAAIZGGAIAAIZGGAIAAIZGGAIAAIZGGAIAAIZGGAIAAIZGGAIAAIZGGAIAAIZGGAIAAIb2/wCOvLMOEkpx8AAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_theme(style=\"ticks\", palette=\"pastel\")\n", "\n", "sns.barplot(data = df, x=\"congruency\", y=\"rt\",\n", " palette=[\"y\", \"b\"], errorbar = None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 4 }